{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall workflow\n",
    "1. action链接数量找到所有核心概念\n",
    "2. 复合概念，构造概念依赖树\n",
    "3. 输入历史材料，涉及概念过滤，建立与核心概念的依赖关系\n",
    "4. 概念聚类（文章中可能包含多个支线，分离开）\n",
    "5. 生成句子（限制与规则）\n",
    "    1）从已知概念出发\n",
    "    2）根据预期长度，对不同聚类的概念进行分配权重，覆盖所有cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import re\n",
    "import tqdm\n",
    "from pathlib import Path\n",
    "from collections import Counter, namedtuple\n",
    "stop_words = set(nltk.corpus.stopwords.words('english'))\n",
    "from lexer import Sentence\n",
    "import termcolor    # 命令行变色\n",
    "import inflect      # 判断单词复数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.513607609220388"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Default_IDF_Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$1+1$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11.4\n",
    "文本摘要和课程大纲的区别\n",
    "规则/约束\n",
    "验证，课程大纲是需要规则的\n",
    "规则的作用，效果（评价）\n",
    "为什么需要规则？\n",
    "\n",
    "否则，学生以现有知识水平理解不了。\n",
    "满足了一些额外的评价指标（课程大纲贴切的），合理性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cats 是 cat 的复数形式\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# TODO: 数据预处理\n",
    "# '-'删掉，但是first-normal-form，先变成空格，再判断每个词是不都是词，如果存在非单词的，就合并\n",
    "# nltk能不能判断复数，给合并一下\n",
    "\n",
    "import inflect\n",
    "\n",
    "p = inflect.engine()\n",
    "\n",
    "word1 = \"cat\"\n",
    "word2 = \"cats\"\n",
    "\n",
    "# 判断是否为复数\n",
    "if p.singular_noun(word2) == word1:\n",
    "    print(f\"{word2} 是 {word1} 的复数形式\")\n",
    "else:\n",
    "    print(f\"{word2} 不是 {word1} 的复数形式\")\n",
    "\n",
    "# TODO: 词干相同是否也聚类？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingular_noun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Word'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mcount\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Union[int, str, Any]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mgender\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'Union[str, Literal[False]]'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Return the singular of text, where text is a plural noun.\n",
      "\n",
      "If count supplied, then return the singular if count is one of:\n",
      "    1, a, an, one, each, every, this, that or if count is None\n",
      "\n",
      "otherwise return text unchanged.\n",
      "\n",
      "Whitespace at the start and end is preserved.\n",
      "\n",
      ">>> p = engine()\n",
      ">>> p.singular_noun('horses')\n",
      "'horse'\n",
      ">>> p.singular_noun('knights')\n",
      "'knight'\n",
      "\n",
      "Returns False when a singular noun is passed.\n",
      "\n",
      ">>> p.singular_noun('horse')\n",
      "False\n",
      ">>> p.singular_noun('knight')\n",
      "False\n",
      ">>> p.singular_noun('soldier')\n",
      "False\n",
      "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/sln/lib/python3.11/site-packages/inflect/__init__.py\n",
      "\u001b[0;31mType:\u001b[0m      method"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def is_same_wo_singular(word1, word2):\n",
    "    \n",
    "\n",
    "A, is, attr, B\n",
    "\n",
    "\n",
    "\n",
    "A of B, is C\n",
    "\n",
    "(A of B)\n",
    "(B is C)\n",
    "\n",
    "(A of B) is C\n",
    "\n",
    "captain of China is C\n",
    "\n",
    "A   of  cons    C   1\n",
    "\n",
    "sentence 1\n",
    "sentence 2\n",
    "\n",
    "\n",
    "\n",
    "?p.singular_noun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "509237.18s - pydevd: Sending message related to process being replaced timed-out after 5 seconds\n",
      "Looking in indexes: http://mirrors.aliyun.com/pypi/simple/\n",
      "Collecting inflect\n",
      "  Downloading http://mirrors.aliyun.com/pypi/packages/f7/e0/c5684d7c058d8f2a9210c322dee32bd025c11d19e5ba23c82ac9188253f9/inflect-7.4.0-py3-none-any.whl (34 kB)\n",
      "Collecting more-itertools>=8.5.0 (from inflect)\n",
      "  Downloading http://mirrors.aliyun.com/pypi/packages/48/7e/3a64597054a70f7c86eb0a7d4fc315b8c1ab932f64883a297bdffeb5f967/more_itertools-10.5.0-py3-none-any.whl (60 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting typeguard>=4.0.1 (from inflect)\n",
      "  Downloading http://mirrors.aliyun.com/pypi/packages/f2/53/9465dedf2d69fe26008e7732cf6e0a385e387c240869e7d54eed49782a3c/typeguard-4.4.1-py3-none-any.whl (35 kB)\n",
      "Collecting typing-extensions>=4.10.0 (from typeguard>=4.0.1->inflect)\n",
      "  Downloading http://mirrors.aliyun.com/pypi/packages/26/9f/ad63fc0248c5379346306f8668cda6e2e2e9c95e01216d2b8ffd9ff037d0/typing_extensions-4.12.2-py3-none-any.whl (37 kB)\n",
      "Installing collected packages: typing-extensions, more-itertools, typeguard, inflect\n",
      "  Attempting uninstall: typing-extensions\n",
      "    Found existing installation: typing_extensions 4.7.1\n",
      "    Uninstalling typing_extensions-4.7.1:\n",
      "      Successfully uninstalled typing_extensions-4.7.1\n",
      "Successfully installed inflect-7.4.0 more-itertools-10.5.0 typeguard-4.4.1 typing-extensions-4.12.2\n"
     ]
    }
   ],
   "source": [
    "!pip install inflect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: 移除多少词？\n",
    "idf_value_mapper = {}\n",
    "Default_IDF_Value = 0\n",
    "for line in Path('idf.txt').read_text().split('\\n'):\n",
    "    if not line: continue\n",
    "    word, value = line.split('\\t')\n",
    "    value = float(value)\n",
    "    idf_value_mapper[word] = value\n",
    "\n",
    "    Default_IDF_Value += value\n",
    "\n",
    "Default_IDF_Value /= len(idf_value_mapper)\n",
    "\n",
    "def get_idf_value(string: str):\n",
    "    idf_values = []\n",
    "    \n",
    "    for word in (words := string.split(' ')):\n",
    "        if word in idf_value_mapper:\n",
    "            idf_values.append(idf_value_mapper[word])\n",
    "\n",
    "    if len(idf_values):\n",
    "        return sum(idf_values) / len(idf_values)\n",
    "    return Default_IDF_Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 24492/24492 [00:10<00:00, 2272.86it/s]\n"
     ]
    }
   ],
   "source": [
    "ActionType = 'action'\n",
    "AttributeType = 'attribution'\n",
    "ConjunctiveType = 'conjunction'\n",
    "ConstraintType = 'constraint'\n",
    "SequentialType = 'sequential'\n",
    "\n",
    "Link = namedtuple('Link', ['pre', 'ind', 'rtype', 'post', 'position'])\n",
    "\n",
    "def _preprocess_node(node):\n",
    "        '''\n",
    "        Filter the meaningful words.\n",
    "        '''\n",
    "        node = node.replace('-', '')\n",
    "        node = node.lower()\n",
    "        words = []\n",
    "        word_pattern = re.compile('(\\d*[a-zA-Z]{3,}\\d*|<b>)')\n",
    "        \n",
    "        for word, tag in nltk.pos_tag(nltk.tokenize.word_tokenize(node)):\n",
    "            if tag not in ['NN', 'NNS', 'VBG', 'JJ', 'CD', 'VBZ', 'VBN', 'RB', 'JJS', 'JJR', 'VB', 'RBR', 'RBS', 'VBD', 'PDT', 'VBP', 'RP', 'IN']:\n",
    "                continue\n",
    "            # the word starts with a alphabet\n",
    "            if not word_pattern.match(word) or word in stop_words or len(word) <= 2:\n",
    "                continue\n",
    "            words.append(word)\n",
    "\n",
    "        return ' '.join(words)\n",
    "\n",
    "def load_links(file):\n",
    "    links = []\n",
    "    for line in tqdm.tqdm(file.read_text().split('\\n')):\n",
    "        if not line: continue\n",
    "        pre, ind, rtype, post, position = line.split('\\t')\n",
    "        \n",
    "        pre, ind, post = _preprocess_node(pre), _preprocess_node(ind), _preprocess_node(post)\n",
    "        if not all([pre, post]) or pre == post: continue\n",
    "        \n",
    "        links.append(Link(pre, ind, rtype, post, position))\n",
    "    return links\n",
    "\n",
    "core_concepts = set()\n",
    "action_link_counter = {}\n",
    "tf_link_counter = {}\n",
    "main_links = load_links(Path('rsm.triplets'))\n",
    "for pre, ind, rtype, post, _ in main_links:\n",
    "    if rtype == ActionType:\n",
    "        action_link_counter[pre] = action_link_counter.get(pre, []) + [ind]\n",
    "    tf_link_counter[pre] = tf_link_counter.get(pre, []) + [ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resource space model 86\n",
      "resource space 74\n",
      "resources 43\n",
      "semantic link network 43\n",
      "resource 39\n",
      "node 34\n",
      "peer 32\n",
      "operations 25\n",
      "rs2 24\n",
      "users 23\n",
      "axis 21\n",
      "people 20\n",
      "one 19\n",
      "chapter 17\n",
      "approach 14\n",
      "network 14\n",
      "theorem 14\n",
      "coordinate 13\n",
      "semantic link 13\n",
      "tree 13\n",
      "resource spaces 12\n",
      "space 12\n",
      "example 12\n",
      "query 12\n",
      "probabilistic resource space model 12\n",
      "nodes 12\n",
      "peers 12\n",
      "point 11\n",
      "rsm 11\n",
      "resource indices 11\n",
      "external relationships 10\n",
      "resource space calculus 10\n",
      "best algorithms 10\n",
      "hierarchy semantics 10\n",
      "structured rsm 10\n",
      "relational database model 9\n",
      "neighbors 9\n",
      "one peer 9\n",
      "applications 8\n",
      "user 8\n",
      "operation 8\n",
      "rs1 8\n",
      "alpha expression 8\n",
      "space dimension 8\n",
      "new node 8\n",
      "object 7\n",
      "system 7\n",
      "coordinates 7\n",
      "data cube 7\n",
      "algorithm 7\n",
      "networks 7\n",
      "community 7\n",
      "files 6\n",
      "classification 6\n",
      "method 6\n",
      "concepts 6\n",
      "way 6\n",
      "rs2rs2 6\n",
      "axes 6\n",
      "two resource spaces 6\n",
      "relational data model 6\n",
      "process 6\n",
      "relational calculus 6\n",
      "results 6\n",
      "worst case 6\n",
      "resource index 6\n",
      "messages 6\n",
      "goods 5\n",
      "world wide web 5\n",
      "common axes 5\n",
      "view 5\n",
      "dimension 5\n",
      "designers 5\n",
      "redundancy 5\n",
      "category 5\n",
      "problem 5\n",
      "table 5\n",
      "semantic model 5\n",
      "graph 5\n",
      "quotient semantic link network 5\n",
      "data warehouse 5\n",
      "section 5\n",
      "routing process 5\n",
      "gossip 5\n",
      "new peer 5\n",
      "limit 5\n",
      "framework 5\n",
      "plications 5\n",
      "customers 4\n",
      "children 4\n",
      "storing 4\n",
      "databases 4\n",
      "systems 4\n",
      "web pages 4\n",
      "keywords 4\n",
      "existing resource space 4\n",
      "merge operation 4\n",
      "many relational tables 4\n",
      "application 4\n",
      "designer 4\n",
      "new resource space 4\n",
      "objects 4\n",
      "readers 4\n",
      "complexity 4\n",
      "inconsistent semantic links 4\n",
      "following theorem 4\n",
      "definition 4\n",
      "disjoin 4\n",
      "split 4\n",
      "nine operations 4\n",
      "mechanism 4\n",
      "resource space algebra 4\n",
      "ontology 4\n",
      "answers 4\n",
      "comparisons 4\n",
      "novel multidimensional indexing structure 4\n",
      "leaf node 4\n",
      "rsm schema tree 4\n",
      "concept tree 4\n",
      "proof 4\n",
      "regular expression 4\n",
      "time 4\n",
      "mbr 4\n",
      "zone 4\n",
      "queries 4\n",
      "previously mentioned style refreshment 4\n",
      "timer 4\n",
      "unstructured networks 4\n",
      "gossip mechanisms 4\n",
      "partition tree 4\n",
      "query initiator 4\n",
      "virus 4\n",
      "probability 4\n",
      "rule 4\n",
      "web resource space model 3\n",
      "categories 3\n",
      "central control 3\n",
      "web 3\n",
      "researchers 3\n",
      "axis name 3\n",
      "split operation 3\n",
      "join operation 3\n",
      "high probability 3\n",
      "commands 3\n",
      "integrity constraints 3\n",
      "relational database 3\n",
      "internal feature 3\n",
      "experiments 3\n",
      "level 3\n",
      "two low dimensional spaces 3\n",
      "examples 3\n",
      "important role 3\n",
      "ddl 3\n",
      "existing resources 3\n",
      "content 3\n",
      "interesting issue 3\n",
      "database 3\n",
      "xml file 3\n",
      "book 3\n",
      "steps 3\n",
      "dotted circles 3\n",
      "union view 3\n",
      "model 3\n",
      "axis resource classes 3\n",
      "given resource space 3\n",
      "corre 3\n",
      "merge 3\n",
      "calculus 3\n",
      "operation selection 3\n",
      "points 3\n",
      "resource operation language rol 3\n",
      "rol 3\n",
      "reduction algorithm 3\n",
      "desirable points 3\n",
      "selection operation 3\n",
      "formula 3\n",
      "value 3\n",
      "lower bound 3\n",
      "merge sort 3\n",
      "alphabet 3\n",
      "following conclusion 3\n",
      "spatial indexing structures 3\n",
      "specific multidimensional access method 3\n",
      "resource operations 3\n",
      "bit strings 3\n",
      "input 3\n",
      "given resource 3\n",
      "bit 3\n",
      "two coordinates 3\n",
      "routing procedure 3\n",
      "techniques 3\n",
      "bootstrap node 3\n",
      "coordinate selection method 3\n",
      "message 3\n",
      "routing performance 3\n",
      "mechanisms 3\n",
      "introducer 3\n",
      "issuing message 3\n",
      "random networks 3\n",
      "network loads 3\n",
      "also basic method 2\n",
      "windows 2\n",
      "efficiency 2\n",
      "function 2\n",
      "semantic data model 2\n",
      "path syntax 2\n",
      "relational tables 2\n",
      "web resources 2\n",
      "relational databases 2\n",
      "advantages 2\n",
      "world 2\n",
      "complex objects 2\n",
      "limitations 2\n",
      "data mining techniques 2\n",
      "knowledge 2\n",
      "service resources 2\n",
      "whole resource space 2\n",
      "specific method 2\n",
      "name space 2\n",
      "web page 2\n",
      "corresponding concepts 2\n",
      "one resource space 2\n",
      "merge condition 2\n",
      "several strategies 2\n",
      "join 2\n",
      "3nf 2\n",
      "human behavior mode 2\n",
      "mode 2\n",
      "query language 2\n",
      "domain ontology 2\n",
      "split command 2\n",
      "rsn 2\n",
      "specified resource 2\n",
      "following update statement 2\n",
      "view name 2\n",
      "axism 2\n",
      "clause 2\n",
      "attribute 2\n",
      "area 2\n",
      "topic 2\n",
      "new way 2\n",
      "multilayer tables 2\n",
      "higher layers 2\n",
      "lower layers 2\n",
      "profes sor 2\n",
      "traditional relational data model 2\n",
      "contents 2\n",
      "resource space model system 2\n",
      "html 2\n",
      "dunhuang cave content space 2\n",
      "external feature 2\n",
      "classification viewpoint 2\n",
      "following process 2\n",
      "new paper 2\n",
      "known classification 2\n",
      "papers 2\n",
      "resource dictionary 2\n",
      "consistency 2\n",
      "following abstract concepts 2\n",
      "first step 2\n",
      "basic category 2\n",
      "different spaces 2\n",
      "abstraction 2\n",
      "analogy 2\n",
      "experience 2\n",
      "basis 2\n",
      "independency checking tool 2\n",
      "orthogonality checking tool 2\n",
      "analogical reasoning 2\n",
      "student 2\n",
      "relational table 2\n",
      "key 2\n",
      "models 2\n",
      "single semantic image 2\n",
      "developers 2\n",
      "semantic communities 2\n",
      "semantic locality 2\n",
      "two basic approaches 2\n",
      "attributes 2\n",
      "coordinate tree 2\n",
      "class 2\n",
      "applicatoin developers 2\n",
      "data 2\n",
      "differences 2\n",
      "following approaches 2\n",
      "undetermined coordinates 2\n",
      "two ways 2\n",
      "indexing techniques 2\n",
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      "two metrics 1\n",
      "epidemic algorithm 1\n",
      "larger views 1\n",
      "smaller views 1\n",
      "hyperlink network 1\n",
      "researches 1\n",
      "certain critical threshold 1\n",
      "certain threshold 1\n",
      "end peer 1\n",
      "random graph 1\n",
      "simulation 1\n",
      "gossip networks 1\n",
      "one category 1\n",
      "average network load 1\n",
      "horizontal axis 1\n",
      "vertical axis 1\n",
      "similar way 1\n",
      "network load 1\n",
      "rsmgossip5 1\n",
      "proposed mechanisms 1\n",
      "dis seminated message 1\n",
      "rationale 1\n",
      "average 1\n",
      "gossip mechanism 1\n",
      "api interface 1\n",
      "semantic vector extractor 1\n",
      "neighborhood list maintainer 1\n",
      "charge 1\n",
      "gossip management module 1\n",
      "proposed gossip mechanisms 1\n",
      "appropriate levels 1\n",
      "level view 1\n",
      "escience application 1\n",
      "various resources 1\n",
      "unstructured network 1\n",
      "two strategies 1\n",
      "classification methods 1\n",
      "automatic classification algo rithms 1\n",
      "cim 1\n",
      "hierarchy 1\n",
      "scientific publications 1\n",
      "rdb 1\n",
      "probabil ity 1\n",
      "following rol statement 1\n",
      "issued point query statement 1\n",
      "membership probabilities 1\n",
      "following statement 1\n",
      "statement 1\n",
      "meanings 1\n",
      "point query statement 1\n",
      "issued resource query statement 1\n",
      "classes=c2 1\n",
      "rs2 rs2 1\n",
      "constraint rules 1\n",
      "new rules 1\n",
      "accurate resource location 1\n",
      "one resource 1\n",
      "nonnull point 1\n",
      "previous probabilistic relational data models 1\n",
      "previous probabilistic relational data models usual 1\n",
      "integrity constraint 1\n",
      "integrity constraint rules 1\n",
      "modeling relational data 1\n",
      "two categories 1\n",
      "resulting models 1\n",
      "classical relational model 1\n",
      "whole tuple 1\n",
      "tuples 1\n",
      "attribute value probabilities 1\n",
      "non1nf probabilistic relational models 1\n",
      "attempt 1\n",
      "non1nf equivalent 1\n",
      "probview 1\n",
      "integral manner 1\n",
      "xml model 1\n",
      "full complexity analysis 1\n",
      "probabilistic data 1\n",
      "attention 1\n",
      "membership probability functions 1\n",
      "human beings 1\n",
      "simple rsm 1\n",
      "web resource space intrinsic characteristics 1\n",
      "resource management 1\n",
      "two models 1\n",
      "richer semantic data model 1\n",
      "structured resource space solution 1\n",
      "thanks also 1\n",
      "research work 1\n",
      "web ontology mechanisms 1\n"
     ]
    }
   ],
   "source": [
    "for word, inds in sorted(action_link_counter.items(), key = lambda x: -len(x[1])):\n",
    "    print(word, len(inds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resource space model 228\n",
      "resource space 224\n",
      "resources 167\n",
      "coordinates 109\n",
      "axis 105\n",
      "resource 100\n",
      "semantic link network 95\n",
      "peer 95\n",
      "example 93\n",
      "point 88\n",
      "operations 86\n",
      "number 83\n",
      "coordinate 78\n",
      "node 76\n",
      "one 71\n",
      "set 68\n",
      "users 51\n",
      "sln 48\n",
      "nodes 48\n",
      "query 47\n",
      "resource spaces 43\n",
      "type 40\n",
      "axes 39\n",
      "union 38\n",
      "peers 38\n",
      "definition 37\n",
      "rsm 37\n",
      "probability 37\n",
      "classification 36\n",
      "tree 35\n",
      "points 34\n",
      "theorem 33\n",
      "people 32\n",
      "neighbors 32\n",
      "approach 31\n",
      "table 31\n",
      "concepts 30\n",
      "difference 30\n",
      "class 29\n",
      "space 28\n",
      "structured rsm 28\n",
      "complexity 27\n",
      "distribution 26\n",
      "information 25\n",
      "semantic link 25\n",
      "network 25\n",
      "external relationships 25\n",
      "bit string 25\n",
      "hierarchy semantics 24\n",
      "level 23\n",
      "semantics 23\n",
      "probabilistic resource space model 23\n",
      "applications 22\n",
      "view 22\n",
      "disjoin 22\n",
      "results 22\n",
      "resource indices 22\n",
      "algorithm 21\n",
      "category 21\n",
      "process 21\n",
      "semantic links 21\n",
      "relationship 21\n",
      "distance 21\n",
      "searching complexity 21\n",
      "categories 20\n",
      "data 20\n",
      "relational database model 20\n",
      "common axes 20\n",
      "selection 20\n",
      "alpha expression 20\n",
      "way 19\n",
      "object 19\n",
      "community 19\n",
      "merge 19\n",
      "dimension 19\n",
      "content 18\n",
      "value 18\n",
      "chapter 18\n",
      "resource class hierarchy 18\n",
      "operation 18\n",
      "resource space calculus 18\n",
      "mbr 18\n",
      "zone 18\n",
      "case 17\n",
      "path 17\n",
      "time 17\n",
      "intersection 17\n",
      "problem 17\n",
      "completeness 17\n",
      "theory 16\n",
      "attributes 16\n",
      "normalization 16\n",
      "concept 16\n",
      "relational calculus 16\n",
      "extended cartesian product 16\n",
      "projection 16\n",
      "bit strings 16\n",
      "efficiency 15\n",
      "method 15\n",
      "comparison 15\n",
      "join 15\n",
      "split 15\n",
      "design 15\n",
      "new resource space 15\n",
      "bit 15\n",
      "networks 15\n",
      "partition tree 15\n",
      "one peer 15\n",
      "mapping 14\n",
      "resource index 14\n",
      "two resource spaces 14\n",
      "system 14\n",
      "union view 14\n",
      "data cube 14\n",
      "space dimension 14\n",
      "mechanisms 14\n",
      "contents 13\n",
      "goods 13\n",
      "objects 13\n",
      "name 13\n",
      "papers 13\n",
      "expressiveness 13\n",
      "graph 13\n",
      "ops 13\n",
      "worst case 13\n",
      "message 13\n",
      "files 12\n",
      "database 12\n",
      "world wide web 12\n",
      "keywords 12\n",
      "user 12\n",
      "designers 12\n",
      "query languages 12\n",
      "calculus 12\n",
      "pair 12\n",
      "quotient semantic link network 12\n",
      "comparisons 12\n",
      "resource space algebra 12\n",
      "order 11\n",
      "file system 11\n",
      "structure 11\n",
      "databases 11\n",
      "relational data model 11\n",
      "use 11\n",
      "two coordinates 11\n",
      "merge operation 11\n",
      "cij 11\n",
      "classification semantics 11\n",
      "redundancy 11\n",
      "two nodes 11\n",
      "perspective 11\n",
      "performance 11\n",
      "lower bound 11\n",
      "rsm schema tree 11\n",
      "concept tree 11\n",
      "zones 11\n",
      "neighbor 11\n",
      "new node 11\n",
      "valid single zone 11\n",
      "advantages 10\n",
      "characteristics 10\n",
      "split operation 10\n",
      "application 10\n",
      "new paper 10\n",
      "key 10\n",
      "semantic model 10\n",
      "model 10\n",
      "data warehouse 10\n",
      "queries 10\n",
      "upper bound 10\n",
      "best algorithms 10\n",
      "multidimensional bit string space 10\n",
      "two concepts 10\n",
      "new peer 10\n",
      "messages 10\n",
      "random networks 10\n",
      "plications 10\n",
      "management 9\n",
      "dimensions 9\n",
      "systems 9\n",
      "scale 9\n",
      "web pages 9\n",
      "real world 9\n",
      "result 9\n",
      "join operation 9\n",
      "input 9\n",
      "rsn 9\n",
      "attribute 9\n",
      "abstraction 9\n",
      "law 9\n",
      "high level 9\n",
      "algebra 9\n",
      "apartment 9\n",
      "rule 9\n",
      "fact 9\n",
      "length 9\n",
      "disjoin operation 9\n",
      "formula 9\n",
      "lgn 9\n",
      "times 9\n",
      "proof 9\n",
      "size 9\n",
      "routing process 9\n",
      "gossip mechanisms 9\n",
      "customers 8\n",
      "experience 8\n",
      "children 8\n",
      "web 8\n",
      "kind 8\n",
      "problems 8\n",
      "normal form 8\n",
      "domain ontology 8\n",
      "given resource space 8\n",
      "relational database 8\n",
      "first normal form 8\n",
      "normal forms 8\n",
      "basis 8\n",
      "relations 8\n",
      "power 8\n",
      "query operations 8\n",
      "semantic web 8\n",
      "inconsistent semantic links 8\n",
      "sub 8\n",
      "edges 8\n",
      "rol 8\n",
      "relational algebra 8\n",
      "operation set 8\n",
      "expression 8\n",
      "range 8\n",
      "minimum 8\n",
      "bytes 8\n",
      "tree structure 8\n",
      "virus 8\n",
      "also basic method 7\n",
      "world 7\n",
      "multidimensional data model 7\n",
      "researchers 7\n",
      "relation 7\n",
      "classifications 7\n",
      "names 7\n",
      "one resource space 7\n",
      "merge condition 7\n",
      "high probability 7\n",
      "existing resource space 7\n",
      "list 7\n",
      "alphabetical order 7\n",
      "photos 7\n",
      "formation 7\n",
      "internal feature 7\n",
      "designer 7\n",
      "dimensionality 7\n",
      "important role 7\n",
      "ing 7\n",
      "net 7\n",
      "search 7\n",
      "math 7\n",
      "construction process 7\n",
      "languages 7\n",
      "directed graph 7\n",
      "analysis 7\n",
      "operation selection 7\n",
      "nine operations 7\n",
      "mechanism 7\n",
      "resource operation language rol 7\n",
      "series 7\n",
      "alphabet 7\n",
      "deletion 7\n",
      "preorder traverse 7\n",
      "ith dimension 7\n",
      "introducer 7\n",
      "techniques 7\n",
      "node zone 7\n",
      "fanout 7\n",
      "power law 7\n",
      "framework 7\n",
      "membership probability functions 7\n",
      "web resource space model 6\n",
      "function 6\n",
      "storing 6\n",
      "correctness 6\n",
      "form 6\n",
      "relational tables 6\n",
      "application scope 6\n",
      "area 6\n",
      "index 6\n",
      "knowledge 6\n",
      "storage 6\n",
      "views 6\n",
      "knowledge grid 6\n",
      "human behavior mode 6\n",
      "degree 6\n",
      "commands 6\n",
      "answer 6\n",
      "terms 6\n",
      "acm computing classification system 6\n",
      "fine classification 6\n",
      "many relational tables 6\n",
      "paper 6\n",
      "experiments 6\n",
      "effect 6\n",
      "top level 6\n",
      "resource partition 6\n",
      "different spaces 6\n",
      "two low dimensional spaces 6\n",
      "gender 6\n",
      "relational table 6\n",
      "interesting issue 6\n",
      "hand 6\n",
      "integration 6\n",
      "readers 6\n",
      "searching efficiency 6\n",
      "several semantic models 6\n",
      "traditional models 6\n",
      "transformation 6\n",
      "isolated nodes 6\n",
      "edge 6\n",
      "classes 6\n",
      "resource class hierarchies 6\n",
      "many operations 6\n",
      "axis resource classes 6\n",
      "addition 6\n",
      "relational model 6\n",
      "certain resource space 6\n",
      "cartesian product 6\n",
      "selection operation 6\n",
      "null point 6\n",
      "point variable 6\n",
      "ontology 6\n",
      "intrinsic complexity 6\n",
      "novel multidimensional indexing structure 6\n",
      "leaf node 6\n",
      "given resource 6\n",
      "bits 6\n",
      "residual bits 6\n",
      "first bit 6\n",
      "unstructured networks 6\n",
      "one neighbor 6\n",
      "one node 6\n",
      "scaling results 6\n",
      "bootstrap node 6\n",
      "important thing 6\n",
      "smallest number 6\n",
      "two zones 6\n",
      "previously mentioned style refreshment 6\n",
      "leaf 6\n",
      "communities 6\n",
      "semantic data model 5\n",
      "central control 5\n",
      "olap 5\n",
      "page 5\n",
      "publishers 5\n",
      "name space 5\n",
      "coordinate tree 5\n",
      "two axes 5\n",
      "two operations 5\n",
      "two 5\n",
      "contrast 5\n",
      "rsi 5\n",
      "viewpoint 5\n",
      "characteristic 5\n",
      "external feature 5\n",
      "external features 5\n",
      "group 5\n",
      "domain 5\n",
      "examples 5\n",
      "student 5\n",
      "single semantic image 5\n",
      "reference 5\n",
      "importance 5\n",
      "questions 5\n",
      "semantic overlay 5\n",
      "applicatoin developers 5\n",
      "ddl 5\n",
      "autonomy 5\n",
      "uncertainty 5\n",
      "book 5\n",
      "semantic link network sln 5\n",
      "cim 5\n",
      "male 5\n",
      "students 5\n",
      "cording 5\n",
      "sln 5\n",
      "steps 5\n",
      "hierarchical coordinates 5\n",
      "work 5\n",
      "existence 5\n",
      "resource classes 5\n",
      "semantic link work 5\n",
      "physics 5\n",
      "external relationship 5\n",
      "alpha expressions 5\n",
      "subset 5\n",
      "possible query results 5\n",
      "opt 5\n",
      "section 5\n",
      "query capability 5\n",
      "two points 5\n",
      "restrictions 5\n",
      "issue 5\n",
      "situation 5\n",
      "answers 5\n",
      "basic approach 5\n",
      "exponential time 5\n",
      "sorting 5\n",
      "searching 5\n",
      "spatial indexing structures 5\n",
      "insertion 5\n",
      "modification 5\n",
      "semantic distance 5\n",
      "current multidimensional access methods 5\n",
      "dex 5\n",
      "blank space 5\n",
      "overlapnum 5\n",
      "routing 5\n",
      "one zone 5\n",
      "charge 5\n",
      "partition 5\n",
      "routing procedure 5\n",
      "one method 5\n",
      "idea 5\n",
      "long link 5\n",
      "timer 5\n",
      "gossip 5\n",
      "classification tree 5\n",
      "ttl 5\n",
      "limit 5\n",
      "whole process 5\n",
      "siblings 5\n",
      "different levels 5\n",
      "network loads 5\n",
      "rdb 5\n",
      "point query 5\n",
      "windows 4\n",
      "development 4\n",
      "web resources 4\n",
      "relational databases 4\n",
      "encapsulation 4\n",
      "complex objects 4\n",
      "limitations 4\n",
      "rules 4\n",
      "resource management 4\n",
      "formal description 4\n",
      "whole resource space 4\n",
      "specific method 4\n",
      "axis name 4\n",
      "corresponding concepts 4\n",
      "name duplication 4\n",
      "four operations 4\n",
      "resource operation language 4\n",
      "basic operations 4\n",
      "mode 4\n",
      "query language 4\n",
      "location 4\n",
      "ursl1 4\n",
      "specified resource 4\n",
      "update 4\n",
      "clause 4\n",
      "conditional expression 4\n",
      "publication 4\n",
      "topics 4\n",
      "finance 4\n",
      "topic 4\n",
      "new way 4\n",
      "human resources 4\n",
      "place 4\n",
      "china 4\n",
      "letter 4\n",
      "purpose 4\n",
      "idf 4\n",
      "resource dictionary 4\n",
      "consistency 4\n",
      "following abstract concepts 4\n",
      "first step 4\n",
      "special case 4\n",
      "spaces 4\n",
      "independency 4\n",
      "identity 4\n",
      "layout relation 4\n",
      "seman tic 4\n",
      "developers 4\n",
      "friends 4\n",
      "two basic approaches 4\n",
      "cost 4\n",
      "existing resources 4\n",
      "differences 4\n",
      "conceptual model 4\n",
      "rela 4\n",
      "following approaches 4\n",
      "fuzzy resource space model 4\n",
      "two ways 4\n",
      "xml file 4\n",
      "proach 4\n",
      "known objects 4\n",
      "semantic ability 4\n",
      "relationships 4\n",
      "entry 4\n",
      "semanticlinks 4\n",
      "distributed versatile resources 4\n",
      "strongly connected component 4\n",
      "strongly connected components 4\n",
      "projections 4\n",
      "three corollaries 4\n",
      "common properties 4\n",
      "impi 4\n",
      "transformation process 4\n",
      "following theorem 4\n",
      "impi 4\n",
      "inclusion relationships 4\n",
      "finite set 4\n",
      "restriction 4\n",
      "axis resource class aci 4\n",
      "resource class set 4\n",
      "part 4\n",
      "corre 4\n",
      "coordi nate 4\n",
      "trends 4\n",
      "decision 4\n",
      "data warehouses 4\n",
      "selection capability 4\n",
      "eight operations 4\n",
      "division 4\n",
      "sql 4\n",
      "rational number field 4\n",
      "subtraction 4\n",
      "multiplication 4\n",
      "result resource space 4\n",
      "certain axis 4\n",
      "also resource space 4\n",
      "existing operations 4\n",
      "result spaces 4\n",
      "conclusion 4\n",
      "sequence 4\n",
      "theoretical results 4\n",
      "reduction algorithm 4\n",
      "desirable points 4\n",
      "two categories 4\n",
      "resource space schema 4\n",
      "resource space system 4\n",
      "variables 4\n",
      "scope 4\n",
      "variable 4\n",
      "qualifiers 4\n",
      "conjunctive clauses 4\n",
      "loss 4\n",
      "tuple 4\n",
      "computation complexity 4\n",
      "merge sort 4\n",
      "tag 4\n",
      "storage manner 4\n",
      "reason 4\n",
      "resource operations 4\n",
      "string 4\n",
      "role 4\n",
      "regular expression 4\n",
      "one time 4\n",
      "compressed encoding method 4\n",
      "byte 4\n",
      "first segment 4\n",
      "next segment 4\n",
      "successive 4\n",
      "one level 4\n",
      "efficient ways 4\n",
      "two cases 4\n",
      "difficulty 4\n",
      "normalized classification semantics 4\n",
      "corresponding key 4\n",
      "cartesian space 4\n",
      "peer neighbors 4\n",
      "address 4\n",
      "addresses 4\n",
      "node neighbors 4\n",
      "ndimensional resource space 4\n",
      "local neighbor states 4\n",
      "guideline 4\n",
      "target 4\n",
      "one part 4\n",
      "coordinate selection method 4\n",
      "valid zone 4\n",
      "associated list 4\n",
      "state 4\n",
      "two joining nodes 4\n",
      "node states 4\n",
      "previously given routing algorithm 4\n",
      "neighbor selection 4\n",
      "next hop 4\n",
      "long links 4\n",
      "takeover message 4\n",
      "increase 4\n",
      "routing performance 4\n",
      "failure recovery mechanism 4\n",
      "overheads 4\n",
      "view size 4\n",
      "proposed mechanisms 4\n",
      "query initiator 4\n",
      "flat gossip mechanism 4\n",
      "fanout value 4\n",
      "researches 4\n",
      "semantic partitions 4\n",
      "rsmgossip3 4\n",
      "rsmgossip5 4\n",
      "disseminated messages 4\n",
      "membership probability distribution 4\n",
      "membership probabilities 4\n",
      "probabil ity 4\n",
      "courses 4\n",
      "probview 4\n",
      "classical relational model patterns 4\n",
      "speed 3\n",
      "machine 3\n",
      "folders 3\n",
      "commonsense 3\n",
      "scientists classification 3\n",
      "simple rsm 3\n",
      "constraints 3\n",
      "study 3\n",
      "dexing 3\n",
      "path syntax 3\n",
      "programming languages 3\n",
      "data mining techniques 3\n",
      "disk 3\n",
      "various resources 3\n",
      "classification method 3\n",
      "books 3\n",
      "different classification methods 3\n",
      "information resources 3\n",
      "service resources 3\n",
      "subspace 3\n",
      "xml 3\n",
      "machine world 3\n",
      "web page 3\n",
      "existing taxonomy 3\n",
      "sets 3\n",
      "domain concepts 3\n",
      "common ancestor 3\n",
      "following three normal forms 3\n",
      "several strategies 3\n",
      "integrity constraints 3\n",
      "drop command 3\n",
      "indices 3\n",
      "following command 3\n",
      "check clause 3\n",
      "constraint 3\n",
      "split command 3\n",
      "following statement 3\n",
      "following update statement 3\n",
      "view name 3\n",
      "select 3\n",
      "text 3\n",
      "browsers 3\n",
      "multiple times 3\n",
      "assistant 3\n",
      "tables 3\n",
      "database systems 3\n",
      "exam 3\n",
      "ple 3\n",
      "new coordinates 3\n",
      "resource space model system 3\n",
      "levels 3\n",
      "region 3\n",
      "latitude 3\n",
      "category axis 3\n",
      "bioinformation 3\n",
      "bioinformation databases 3\n",
      "dunhuang cave content space 3\n",
      "cave number 3\n",
      "one resource 3\n",
      "classification viewpoint 3\n",
      "publisher 3\n",
      "journal name 3\n",
      "impact factor 3\n",
      "certain number 3\n",
      "meaning 3\n",
      "low dimensional resource spaces 3\n",
      "resource analysis 3\n",
      "abstract attribute 3\n",
      "first level 3\n",
      "basic category 3\n",
      "existing spaces 3\n",
      "new space 3\n",
      "new points 3\n",
      "analogy 3\n",
      "ontology repository 3\n",
      "independency checking tool 3\n",
      "independency checking 3\n",
      "orthogonality checking tool 3\n",
      "analogical reasoning 3\n",
      "key dimension 3\n",
      "table1 3\n",
      "values 3\n",
      "one key 3\n",
      "two resources 3\n",
      "semantic description 3\n",
      "programs 3\n",
      "models 3\n",
      "semantic network 3\n",
      "large scale 3\n",
      "computers 3\n",
      "relevant concepts 3\n",
      "semantic locality 3\n",
      "ideal data model 3\n",
      "whole 3\n",
      "representation 3\n",
      "class operations 3\n",
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      "birds 2\n",
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      "using 1\n",
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      "eugster 1\n",
      "lin 1\n",
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      "example neighbors 1\n",
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      "universe resource 1\n",
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      "duda 1\n",
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      "genetic algorithms 1\n",
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      "static partition 1\n",
      "benefits 1\n",
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      "issuing 1\n",
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      "consideration 1\n",
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      "one peer category 1\n",
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      "one randomly selected neighbor 1\n",
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      "also leaves 1\n",
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      "two communities 1\n",
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      "different mechanisms 1\n",
      "neighbor lists 1\n",
      "applica tions 1\n",
      "peer interests 1\n",
      "neighbor list 1\n",
      "need 1\n",
      "appropriate category 1\n",
      "several gossip processes 1\n",
      "trade 1\n",
      "whole network cost 1\n",
      "discrete data space 1\n",
      "corresponding components 1\n",
      "many components 1\n",
      "specified coordinate set 1\n",
      "correlative metric 1\n",
      "simplicity 1\n",
      "sizes 1\n",
      "peer crash 1\n",
      "given peer 1\n",
      "query initiator view 1\n",
      "source 1\n",
      "query initiator views 1\n",
      "total rounds 1\n",
      "gossip style 1\n",
      "entire group 1\n",
      "logn• 1\n",
      "fanout+1/log 1\n",
      "round 1\n",
      "subpartitions 1\n",
      "hop count 1\n",
      "message dissemination ttl1 1\n",
      "gossip mech anisms 1\n",
      "two kinds 1\n",
      "directed networks 1\n",
      "experiment 1\n",
      "different parameters 1\n",
      "average value 1\n",
      "appropriate view sizes 1\n",
      "gossip systems 1\n",
      "promising effect 1\n",
      "epidemic algorithm 1\n",
      "larger views 1\n",
      "smaller views 1\n",
      "many large networks 1\n",
      "hyperlink network 1\n",
      "kmax 1\n",
      "exponent 1\n",
      "hubs 1\n",
      "start peer 1\n",
      "average neighbors 1\n",
      "flatgossip 1\n",
      "different number 1\n",
      "one category 1\n",
      "several categories 1\n",
      "equal size 1\n",
      "parameters 1\n",
      "horizontal axis 1\n",
      "parameter ttl 1\n",
      "vertical axis 1\n",
      "figures 1\n",
      "previous algorithms 1\n",
      "dis seminated message 1\n",
      "architecture 1\n",
      "api interface 1\n",
      "semantic vector extractor 1\n",
      "technologies 1\n",
      "information retrieval area 1\n",
      "vector space model 1\n",
      "neighborhood list maintainer 1\n",
      "gossip management module 1\n",
      "proposed gossip mechanisms 1\n",
      "new decentralized resource sharing mechanism 1\n",
      "resource space model normalization theory 1\n",
      "integrity theory 1\n",
      "lan 1\n",
      "operation guage 1\n",
      "theoretical analysis 1\n",
      "feasibility 1\n",
      "probabilistic event 1\n",
      "probabilistic distribution 1\n",
      "resource membership probability distribution 1\n",
      "membership probability function 1\n",
      "membership probabilistic function 1\n",
      "belonging 1\n",
      "hierarchical coordinate 1\n",
      "child coordinate 1\n",
      "belong ing 1\n",
      "scientific publications 1\n",
      "conditional probability 1\n",
      "subcoordinate 1\n",
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      "probability point 1\n",
      "general definition 1\n",
      "total probability theorem 1\n",
      "following constraints 1\n",
      "noun phase 1\n",
      "usually logical combination 1\n",
      "logical combination 1\n",
      "projec tions 1\n",
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      "real number 1\n",
      "logical expression combination 1\n",
      "following form 1\n",
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      "point query statement 1\n",
      "classical resource space model 1\n",
      "students classes 1\n",
      "axis classes 1\n",
      "classes=c2 1\n",
      "axes courses 1\n",
      "delete operation 1\n",
      "following operation 1\n",
      "noncommon axis 1\n",
      "coordinate system 1\n",
      "two nonnull points 1\n",
      "given point 1\n",
      "resource dependency 1\n",
      "previous probabilistic relational data models 1\n",
      "entities one 1\n",
      "dependency 1\n",
      "probabilistic events 1\n",
      "entity integrity constraint 1\n",
      "membership integrity constraint 1\n",
      "tegrity 1\n",
      "merge operations 1\n",
      "membership probabilistic functions respectively 1\n",
      "uncertain data 1\n",
      "context 1\n",
      "resulting models 1\n",
      "cavallo 1\n",
      "dey 1\n",
      "fuhr 1\n",
      "non1nf associate probabilities 1\n",
      "information loss 1\n",
      "combinatorial explosion 1\n",
      "attribute value probabilities 1\n",
      "complicated algebras 1\n",
      "non1nf data 1\n",
      "non1nf equivalent 1\n",
      "evaluation 1\n",
      "probabilistic database 1\n",
      "accuracy 1\n",
      "lineage 1\n",
      "integral manner 1\n",
      "protdb 1\n",
      "stochastic events 1\n",
      "xml data 1\n",
      "nierman 1\n",
      "xml documents 1\n",
      "senellart 1\n",
      "conflicts 1\n",
      "data integration 1\n",
      "membership probability distributions 1\n",
      "classification hierarchies 1\n",
      "general form 1\n",
      "syn thesize 1\n",
      "web semantic data model 1\n",
      "two models 1\n",
      "expressive power 1\n",
      "physical storage mechanism 1\n",
      "previous multidimensional index 1\n",
      "decentralized resource space 1\n",
      "synergy normalization 1\n",
      "erlin yao 1\n",
      "yunpeng xing 1\n",
      "xiang 1\n",
      "chao 1\n",
      "important contribution 1\n",
      "china knowledge grid research group 1\n",
      "china knowledge grid research group help 1\n",
      "national basic research program 1\n",
      "framework project gredia 1\n",
      "international cooperation program 1\n",
      "ministry 1\n",
      "science 1\n",
      "promising model 1\n",
      "database models 1\n",
      "web ontology mechanisms 1\n",
      "powerful semantic platform 1\n"
     ]
    }
   ],
   "source": [
    "for word, inds in sorted(tf_link_counter.items(), key = lambda x: -len(x[1])):\n",
    "    print(word, len(inds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 100 / 1127 Core concepts, the weight is $|Action(n)| * IDF(n)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1127\n",
      "peer 32\n",
      "resource space model 85\n",
      "resource space 71\n",
      "resource 39\n",
      "semantic link network 43\n",
      "node 34\n",
      "rsm 11\n",
      "resources 41\n",
      "axis 21\n",
      "chapter 17\n",
      "peers 12\n",
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      "regional information 1\n",
      "disjoin operation 1\n",
      "index 1\n",
      "new way 2\n",
      "relationships 1\n",
      "common axis 1\n",
      "criteria 1\n",
      "first step 2\n",
      "corresponding axis 1\n",
      "smaller views 1\n",
      "sequential searching algorithm 1\n",
      "one sibling concept 1\n",
      "corresponding external memory storage 1\n",
      "efficient resource management 1\n",
      "artificial intelligence research 1\n",
      "three measurements 1\n",
      "performance 2\n",
      "semantic data model 2\n",
      "polynomial time complexity 1\n",
      "necessary modifications 1\n",
      "minimum 1\n",
      "concept hierarchy semantics 1\n",
      "linear storage space 1\n",
      "two cases 2\n",
      "resource attributes 1\n",
      "old points 1\n",
      "resource space model designers 1\n",
      "following process 2\n",
      "non1nf probabilistic relational models 1\n",
      "select statement 1\n",
      "theoretical basis 1\n",
      "underlying indexing structure 1\n",
      "following command 1\n",
      "challenges 1\n",
      "new concept refinement 1\n",
      "external memory 1\n",
      "computation complexity 1\n",
      "concept hierarchy tree 1\n",
      "element 1\n",
      "reasoning rule 1\n",
      "path name 1\n",
      "average network load 1\n",
      "exponential time 1\n",
      "web resource space intrinsic characteristics 1\n",
      "two policies 1\n",
      "better grouping 1\n",
      "strategies 1\n",
      "newly defined operations 1\n",
      "given bit strings 1\n",
      "class hierarchy 1\n",
      "specified conditions 1\n",
      "typical query 1\n",
      "algorithm binary insertion 1\n",
      "neighbor information 1\n",
      "larger views 1\n",
      "semantic vector extractor 1\n",
      "computer 1\n",
      "point query statement 1\n",
      "desired points 1\n",
      "low dimensional resource spaces 1\n",
      "existing spaces 1\n",
      "select clause 1\n",
      "relational database system 1\n",
      "new paper 2\n",
      "names 1\n",
      "least upper bound 1\n",
      "memory 1\n",
      "geographical resource space system 1\n",
      "abstraction semantic relations 1\n",
      "grid computing research 1\n",
      "generic classes 1\n",
      "greedy method 1\n",
      "product 1\n",
      "efficient resource operations 1\n",
      "computing 1\n",
      "block size 1\n",
      "poor definition 1\n",
      "accurate resource location 1\n",
      "attempt 1\n",
      "resource class hierarchy 1\n",
      "one peer information 1\n",
      "non1nf equivalent 1\n",
      "multidimensional data model 1\n",
      "query name 1\n",
      "concepts semantics 1\n",
      "xml model 1\n",
      "fuzzy resource space model 1\n",
      "web page content 1\n",
      "programming languages 1\n",
      "mapping 1\n",
      "two resource class hierarchies 1\n",
      "polynomial time algorithm 1\n",
      "two commonly used bit string operators 1\n",
      "relational database model normal form theory 1\n",
      "relational database application systems 1\n",
      "matrix 1\n",
      "one part 2\n",
      "various indexing techniques 1\n",
      "original resource spaces 1\n",
      "certain critical threshold 1\n",
      "basic operations 1\n",
      "application scope 1\n",
      "sufficient detail 1\n",
      "formal description 1\n",
      "newly created resource space 1\n",
      "class operations 1\n",
      "constraint rules 1\n",
      "result spaces 1\n",
      "impact factor 1\n",
      "third condition 1\n",
      "setup 1\n",
      "semantic community phenomenon 1\n",
      "attribute value probabilities 1\n",
      "tree nodes 1\n",
      "solution 1\n",
      "null information 1\n",
      "smallest one 1\n",
      "different granularity levels 1\n",
      "resource view 1\n",
      "data 2\n",
      "information 2\n",
      "proposed mechanisms 1\n",
      "hierarchical classification semantics 1\n",
      "modeling relational data 1\n",
      "concept world 1\n",
      "previous probabilistic relational data models usual 1\n",
      "long links 1\n",
      "three resource spaces 1\n",
      "semantic reason ing 1\n",
      "existing operations 1\n",
      "semantic link network operations 1\n",
      "new requirement 1\n",
      "operation set regard 1\n",
      "interest 1\n",
      "good references 1\n",
      "single file file_ctree 1\n",
      "long link 1\n",
      "structured resource space solution 1\n",
      "four operations 1\n",
      "objective world 1\n",
      "following statement 1\n",
      "strategy 1\n",
      "random graph 1\n",
      "individual nodes 1\n",
      "structured resource space 1\n",
      "string class 1\n",
      "ability 1\n",
      "following things 1\n",
      "transformation process 1\n",
      "user view 1\n",
      "design 1\n",
      "relevant concepts 1\n",
      "appropriate levels 1\n",
      "given relational database system 1\n",
      "many comparisons 1\n",
      "decision making 1\n",
      "two spaces 1\n",
      "haming distance 1\n",
      "certain threshold 1\n",
      "traditional base 1\n",
      "appropriate algorithms 1\n",
      "issue 1\n",
      "two 2\n",
      "purpose 1\n",
      "group 1\n",
      "entire resource space 1\n",
      "resource space design 1\n",
      "domain concepts 1\n",
      "procedure 1\n",
      "understanding 1\n",
      "score course 1\n",
      "resource space user 1\n",
      "resource space rsm+1 1\n",
      "previous probabilistic relational data models 1\n",
      "classification semantics 1\n",
      "four layers 1\n",
      "thanks also 1\n",
      "base resource class 1\n",
      "also criterion 1\n",
      "structured query language 1\n",
      "semantic node 1\n",
      "resource operation language 1\n",
      "external features 1\n",
      "idea 1\n",
      "database systems 1\n",
      "following definitions 1\n",
      "two papers 1\n",
      "theoretical results 1\n",
      "common nodes 1\n",
      "role 1\n",
      "inview randomly 1\n",
      "probabilistic data 1\n",
      "probability world 1\n",
      "basic idea 1\n",
      "many operations 1\n",
      "reference relation 1\n",
      "best lower bound 1\n",
      "different areas 1\n",
      "metric semantic distance 1\n",
      "whole 1\n",
      "relations 1\n",
      "application develpers 1\n",
      "two solutions 1\n",
      "new node node state 1\n",
      "escience application 1\n",
      "relational data base model 1\n",
      "attention 1\n",
      "various web resources 1\n",
      "corresponding query languages 1\n",
      "database system 1\n",
      "semantic relation search mechanism 1\n",
      "existing database systems 1\n",
      "semantic link network resource space model 1\n",
      "account 1\n",
      "key pair 1\n",
      "good definition 1\n",
      "single binary tree 1\n",
      "binary search algorithm 1\n",
      "two semantic link networks 1\n",
      "new community 1\n",
      "operation set 1\n",
      "problems 1\n",
      "previous sections 1\n",
      "effect 1\n",
      "first condition 1\n",
      "four components 1\n",
      "special semantic link work 1\n",
      "following three normal forms 1\n",
      "however major challenge 1\n",
      "maximum similarity value 1\n",
      "resource space definition language rsdl 1\n",
      "proposed resource space gebra 1\n",
      "research groups 1\n",
      "semantic component 1\n",
      "efficient method 1\n",
      "construction process 1\n",
      "vector 1\n",
      "result resource space 1\n",
      "content classification 1\n",
      "basic semantic relations 1\n",
      "machine world 1\n",
      "richer semantic data model 1\n",
      "full complexity analysis 1\n",
      "best solution 1\n",
      "level view 1\n",
      "lower time cost 1\n",
      "data items 1\n",
      "resource analysis 1\n",
      "one aspect 1\n",
      "several new operations 1\n",
      "less space needs 1\n",
      "multiple ways 1\n",
      "corresponding key 1\n",
      "semantic link work 1\n",
      "various resources 1\n",
      "two strategies 1\n",
      "semantic description 1\n",
      "semantic network 1\n",
      "one semantic link 1\n",
      "structured work 1\n",
      "given resource space given resource space 1\n",
      "knowledge resources 1\n",
      "natural idea 1\n",
      "last two conditions 1\n",
      "three levels 1\n",
      "original resource space model 1\n",
      "table name 1\n",
      "selection process 1\n",
      "classification point 1\n",
      "values 1\n",
      "application systems 1\n",
      "comparison formulas 1\n",
      "resource set 1\n",
      "relevant examples 1\n",
      "basic method 1\n",
      "one popular type 1\n",
      "one resource 1\n",
      "several differences 1\n",
      "new points 1\n",
      "human 1\n",
      "different aspects 1\n",
      "many ways 1\n",
      "detailed information 1\n",
      "best effect 1\n",
      "one category 1\n",
      "three parts 1\n",
      "two metrics 1\n",
      "new rules 1\n",
      "network model 1\n",
      "average 1\n",
      "classification methods 1\n",
      "resulting models 1\n",
      "multiple classification methods 1\n",
      "classification method 1\n",
      "information resources 1\n",
      "structure 1\n",
      "new space 1\n",
      "also key 1\n",
      "two seemly different concepts 1\n",
      "two points 1\n",
      "different categories 1\n",
      "two parts 1\n",
      "state 1\n",
      "best algorithm 1\n",
      "also resources 1\n",
      "first three levels 1\n",
      "required information 1\n",
      "two categories 1\n",
      "terms 1\n",
      "tasks 1\n",
      "new domain 1\n",
      "second step 1\n",
      "different classification methods 1\n",
      "set list 1\n",
      "following two parts 1\n",
      "one classification model 1\n",
      "similar way 1\n",
      "one step 1\n",
      "case 1\n",
      "following method 1\n",
      "second 1\n",
      "one type 1\n",
      "following analysis 1\n",
      "order 1\n",
      "two methods 1\n",
      "different models 1\n",
      "one method 1\n",
      "research work 1\n",
      "two models 1\n",
      "paper 1\n",
      "language 1\n",
      "work 1\n",
      "set 1\n",
      "different epistemologies 1\n",
      "one mbrs 1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(len(action_link_counter))\n",
    "core_concepts = set()\n",
    "concept_weight_mapper = {}\n",
    "\n",
    "for index, (word, inds) in enumerate(sorted(action_link_counter.items(), key = lambda x: -len(x[1]) * get_idf_value(x[0]))):\n",
    "    print(word, len(inds))\n",
    "    if index <= 100:\n",
    "        core_concepts.add(word)\n",
    "        concept_weight_mapper[word] = len(action_link_counter[word]) * get_idf_value(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1127\n",
      "coordinates 109\n",
      "peer 95\n",
      "resource space 224\n",
      "resource space model 228\n",
      "axis 105\n",
      "sln 48\n",
      "resources 167\n",
      "coordinate 78\n",
      "rsm 37\n",
      "operations 86\n",
      "resource 100\n",
      "peers 38\n",
      "semantic link network 95\n",
      "node 76\n",
      "axes 39\n",
      "point 88\n",
      "disjoin 22\n",
      "structured rsm 28\n",
      "theorem 33\n",
      "query 47\n",
      "nodes 48\n",
      "resource spaces 43\n",
      "neighbors 32\n",
      "union 38\n",
      "users 51\n",
      "mbr 18\n",
      "example 93\n",
      "number 83\n",
      "ops 13\n",
      "zone 18\n",
      "definition 37\n",
      "cij 11\n",
      "chapter 18\n",
      "concepts 30\n",
      "one 71\n",
      "set 68\n",
      "tree 35\n",
      "bit string 25\n",
      "rsn 9\n",
      "plications 10\n",
      "alpha expression 20\n",
      "goods 13\n",
      "people 32\n",
      "points 34\n",
      "probability 37\n",
      "completeness 17\n",
      "external relationships 25\n",
      "hierarchy semantics 24\n",
      "relational calculus 16\n",
      "resource indices 22\n",
      "join 15\n",
      "merge 19\n",
      "classification 36\n",
      "complexity 27\n",
      "common axes 20\n",
      "expressiveness 13\n",
      "type 40\n",
      "intersection 17\n",
      "lgn 9\n",
      "designers 12\n",
      "zones 11\n",
      "searching complexity 21\n",
      "calculus 12\n",
      "introducer 7\n",
      "class 29\n",
      "apartment 9\n",
      "projection 16\n",
      "semantics 23\n",
      "extended cartesian product 16\n",
      "semantic link 25\n",
      "rol 8\n",
      "resource space calculus 18\n",
      "difference 30\n",
      "bit 15\n",
      "fanout 7\n",
      "bit strings 16\n",
      "space 28\n",
      "relationship 21\n",
      "network 25\n",
      "view 22\n",
      "rsm schema tree 11\n",
      "relational database model 20\n",
      "dimension 19\n",
      "operation 18\n",
      "path 17\n",
      "community 19\n",
      "probabilistic resource space model 23\n",
      "normalization 16\n",
      "attributes 16\n",
      "one peer 15\n",
      "distribution 26\n",
      "category 21\n",
      "algebra 9\n",
      "semantic links 21\n",
      "distance 21\n",
      "gossip mechanisms 9\n",
      "object 19\n",
      "data cube 14\n",
      "resource class hierarchy 18\n",
      "partition tree 15\n",
      "mechanisms 14\n",
      "virus 8\n",
      "contents 13\n",
      "neighbor 11\n",
      "message 13\n",
      "redundancy 11\n",
      "selection 20\n",
      "efficiency 15\n",
      "applications 22\n",
      "bytes 8\n",
      "rdb 5\n",
      "theory 16\n",
      "preorder traverse 7\n",
      "keywords 12\n",
      "ddl 5\n",
      "rsi 5\n",
      "categories 20\n",
      "data warehouse 10\n",
      "databases 11\n",
      "cim 5\n",
      "photos 7\n",
      "approach 31\n",
      "papers 13\n",
      "concept 16\n",
      "table 31\n",
      "quotient semantic link network 12\n",
      "union view 14\n",
      "math 7\n",
      "files 12\n",
      "resource space algebra 12\n",
      "comparisons 12\n",
      "designer 7\n",
      "dex 5\n",
      "algorithm 21\n",
      "customers 8\n",
      "objects 13\n",
      "resource index 14\n",
      "timer 5\n",
      "messages 10\n",
      "abstraction 9\n",
      "networks 15\n",
      "ttl 5\n",
      "content 18\n",
      "cording 5\n",
      "relational algebra 8\n",
      "gossip 5\n",
      "mapping 14\n",
      "olap 5\n",
      "sln 5\n",
      "overlapnum 5\n",
      "valid single zone 11\n",
      "merge operation 11\n",
      "law 9\n",
      "proof 9\n",
      "split 15\n",
      "design 15\n",
      "level 23\n",
      "new peer 10\n",
      "multidimensional bit string space 10\n",
      "graph 13\n",
      "join operation 9\n",
      "database 12\n",
      "routing process 9\n",
      "space dimension 14\n",
      "two coordinates 11\n",
      "two resource spaces 14\n",
      "autonomy 5\n",
      "management 9\n",
      "coordi nate 4\n",
      "name 13\n",
      "queries 10\n",
      "perspective 11\n",
      "worst case 13\n",
      "value 18\n",
      "process 21\n",
      "formula 9\n",
      "information 25\n",
      "upper bound 10\n",
      "encapsulation 4\n",
      "advantages 10\n",
      "classifications 7\n",
      "independency 4\n",
      "routing 5\n",
      "bits 6\n",
      "world wide web 12\n",
      "new resource space 15\n",
      "alphabet 7\n",
      "ursl1 4\n",
      "semanticlinks 4\n",
      "impi 4\n",
      "impi 4\n",
      "rsmgossip3 4\n",
      "rsmgossip5 4\n",
      "probview 4\n",
      "formation 7\n",
      "residual bits 6\n",
      "node zone 7\n",
      "way 19\n",
      "commands 6\n",
      "overheads 4\n",
      "attribute 9\n",
      "query languages 12\n",
      "deletion 7\n",
      "publishers 5\n",
      "comparison 15\n",
      "lower bound 11\n",
      "children 8\n",
      "dimensionality 7\n",
      "storing 6\n",
      "user 12\n",
      "concept tree 11\n",
      "qualifiers 4\n",
      "characteristics 10\n",
      "edges 8\n",
      "physics 5\n",
      "relational database 8\n",
      "results 22\n",
      "dimensions 9\n",
      "split operation 10\n",
      "disjoin operation 9\n",
      "web pages 9\n",
      "storage 6\n",
      "siblings 5\n",
      "file system 11\n",
      "problem 17\n",
      "ith dimension 7\n",
      "cartesian product 6\n",
      "probabil ity 4\n",
      "leaf 6\n",
      "experience 8\n",
      "corre 4\n",
      "data 20\n",
      "classification semantics 11\n",
      "alphabetical order 7\n",
      "proach 4\n",
      "power law 7\n",
      "communities 6\n",
      "charge 5\n",
      "resource operation language rol 7\n",
      "opt 5\n",
      "case 17\n",
      "time 17\n",
      "ing 7\n",
      "sorting 5\n",
      "seman tic 4\n",
      "nine operations 7\n",
      "power 8\n",
      "net 7\n",
      "rela 4\n",
      "relational data model 11\n",
      "viewpoint 5\n",
      "pair 12\n",
      "query operations 8\n",
      "random networks 10\n",
      "subtraction 4\n",
      "inconsistent semantic links 8\n",
      "merge condition 7\n",
      "takeover timer 3\n",
      "new node 11\n",
      "method 15\n",
      "ontology 6\n",
      "minimum 8\n",
      "takeover message 4\n",
      "domain ontology 8\n",
      "key 10\n",
      "correctness 6\n",
      "ccs 3\n",
      "readers 6\n",
      "normal forms 8\n",
      "dexing 3\n",
      "bioinformation 3\n",
      "mbr1 3\n",
      "inview 3\n",
      "rsmgossip1 3\n",
      "sql 4\n",
      "gorithm 3\n",
      "semantic overlay 5\n",
      "application 10\n",
      "expression 8\n",
      "initiator 3\n",
      "disseminated messages 4\n",
      "folders 3\n",
      "two nodes 11\n",
      "directed graph 7\n",
      "male 5\n",
      "network loads 5\n",
      "membership probability functions 7\n",
      "bootstrap node 6\n",
      "courses 4\n",
      "two zones 6\n",
      "applicatoin developers 5\n",
      "stead 3\n",
      "leaf node 6\n",
      "views 6\n",
      "finance 4\n",
      "routing procedure 5\n",
      "diameter 3\n",
      "gender 6\n",
      "isolated nodes 6\n",
      "series 7\n",
      "novel multidimensional indexing structure 6\n",
      "rule 9\n",
      "guideline 4\n",
      "real world 9\n",
      "unstructured networks 6\n",
      "uncertainty 5\n",
      "relational tables 6\n",
      "web 8\n",
      "table1 3\n",
      "edge 6\n",
      "partition 5\n",
      "data warehouses 4\n",
      "integration 6\n",
      "intrinsic complexity 6\n",
      "query initiator 4\n",
      "scale 9\n",
      "insertion 5\n",
      "hierarchical coordinates 5\n",
      "system 14\n",
      "semantic link network sln 5\n",
      "basis 8\n",
      "fanout value 4\n",
      "resource partition 6\n",
      "searching efficiency 6\n",
      "best algorithms 10\n",
      "two concepts 10\n",
      "normal form 8\n",
      "sub 8\n",
      "three corollaries 4\n",
      "kind 8\n",
      "synergy 3\n",
      "transformation 6\n",
      "projections 4\n",
      "peer neighbors 4\n",
      "latitude 3\n",
      "friends 4\n",
      "structure 11\n",
      "names 7\n",
      "browsers 3\n",
      "alpha expressions 5\n",
      "flat gossip mechanism 4\n",
      "resource class hierarchies 6\n",
      "researches 4\n",
      "multidimensional data model 7\n",
      "times 9\n",
      "acm computing classification system 6\n",
      "student 5\n",
      "rsm schema 3\n",
      "operation selection 7\n",
      "researchers 7\n",
      "mechanism 7\n",
      "levene 2\n",
      "byte 4\n",
      "axis resource classes 6\n",
      "publisher 3\n",
      "spatial indexing structures 5\n",
      "successive 4\n",
      "characteristic 5\n",
      "multiplication 4\n",
      "index 6\n",
      "restrictions 5\n",
      "coor 2\n",
      "cham 2\n",
      "conjunctive clauses 4\n",
      "book 5\n",
      "undetermined coordinates 3\n",
      "searching 5\n",
      "hierar chical 2\n",
      "relations 8\n",
      "ple 3\n",
      "qualification 3\n",
      "students 5\n",
      "length 9\n",
      "integrity 3\n",
      "cin 2\n",
      "coordinate tree 5\n",
      "pha 2\n",
      "blank space 5\n",
      "valid zone 4\n",
      "subspace 3\n",
      "existence 5\n",
      "null point 6\n",
      "semantic web 8\n",
      "developers 4\n",
      "division 4\n",
      "modification 5\n",
      "knowledge grid 6\n",
      "supermarkets 2\n",
      "specialization 3\n",
      "internal feature 7\n",
      "human behavior mode 6\n",
      "independency checking 3\n",
      "atomicity 2\n",
      "axis resource class aci 4\n",
      "problems 8\n",
      "operation set 8\n",
      "one zone 5\n",
      "page 5\n",
      "windows 4\n",
      "world 7\n",
      "dns 2\n",
      "profes sor 2\n",
      "range 8\n",
      "buffer 3\n",
      "publication 4\n",
      "subclasses 3\n",
      "tree structure 8\n",
      "many relational tables 6\n",
      "one neighbor 6\n",
      "chil 2\n",
      "first normal form 8\n",
      "joining peer 3\n",
      "ndimensional resource space 4\n",
      "topk correlative categories 3\n",
      "fact 9\n",
      "two axes 5\n",
      "query capability 5\n",
      "disk 3\n",
      "tuple 4\n",
      "area 6\n",
      "performance 11\n",
      "subclass 3\n",
      "important thing 6\n",
      "selection operation 6\n",
      "cartesian space 4\n",
      "dtds 2\n",
      "dist 3\n",
      "name duplication 4\n",
      "relational model 6\n",
      "scaling results 6\n",
      "relational databases 4\n",
      "relational table 6\n",
      "answers 5\n",
      "given resource space 8\n",
      "ndimensional cartesian space 3\n",
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      "smallest number 6\n",
      "exam 3\n",
      "degree 6\n",
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      "failure recovery mechanism 4\n",
      "simple rsm 3\n",
      "order 11\n",
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      "search 7\n",
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      "overlay 2\n",
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      "rsm schema definition module 3\n",
      "many operations 6\n",
      "gen 3\n",
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      "certain resource space 6\n",
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      "update 4\n",
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      "web resource space model 6\n",
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      "high probability 7\n",
      "corollary 2\n",
      "contacted peer 2\n",
      "entry 4\n",
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      "one node 6\n",
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      "spec 2\n",
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      "string 4\n",
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      "knowledge 6\n",
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      "yin 2\n",
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      "reduction algorithm 4\n",
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      "failed node 3\n",
      "semantic link work 5\n",
      "given rsm 2\n",
      "comparison formulas 3\n",
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      "rela tional database 2\n",
      "several sibling mbrs 3\n",
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      "resource insertion 3\n",
      "topic 4\n",
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      "apart 3\n",
      "finance coordinate 2\n",
      "four operations 4\n",
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      "depth 3\n",
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      "journal name 3\n",
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      "form 6\n",
      "several semantic models 6\n",
      "transformation process 4\n",
      "least load 3\n",
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      "efficient ways 4\n",
      "greatest lower bound 3\n",
      "web resources 4\n",
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      "trans 2\n",
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      "meanings 3\n",
      "heart 2\n",
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      "experiments 6\n",
      "num 2\n",
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      "purpose 4\n",
      "whole process 5\n",
      "check clause 3\n",
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      "tie 2\n",
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      "possible query results 5\n",
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      "dis 2\n",
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      "foundation 3\n",
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      "associate professor 2\n",
      "root 3\n",
      "quotient network 2\n",
      "descriptive meta data 3\n",
      "querying 2\n",
      "timal 1\n",
      "address 4\n",
      "automatic classification algo rithms 2\n",
      "search request 3\n",
      "convenience 2\n",
      "resource operation language 4\n",
      "url 2\n",
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      "evolution 2\n",
      "constraint 3\n",
      "finite set 4\n",
      "set ops 2\n",
      "whole resource space 4\n",
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      "corresponding concepts 4\n",
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      "basic approach 5\n",
      "reason 4\n",
      "load 2\n",
      "ter 2\n",
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      "role 4\n",
      "examples 5\n",
      "axis resource class 3\n",
      "web page 3\n",
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      "path syntax 3\n",
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      "bottom community 3\n",
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      "differences 4\n",
      "self 3\n",
      "consecutive bits 2\n",
      "increase 4\n",
      "thus ena future interconnection environment 2\n",
      "certain depart ment 2\n",
      "tables 3\n",
      "semantic clues 3\n",
      "course 3\n",
      "efficient routing 2\n",
      "fective 1\n",
      "rch 1\n",
      "bitstring 1\n",
      "search request flood 2\n",
      "first segment 4\n",
      "erwise 1\n",
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      "database designer 2\n",
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      "node coordinate zone 2\n",
      "two points 5\n",
      "rules 4\n",
      "following command 3\n",
      "aggregation 2\n",
      "one byte 3\n",
      "probabilistic xml approach 3\n",
      "calligraphy 1\n",
      "persons 2\n",
      "equal zones 2\n",
      "service resources 3\n",
      "logn 1\n",
      "external memory 3\n",
      "nonprocedural query style 3\n",
      "exact query 3\n",
      "query algebra 2\n",
      "redirections 1\n",
      "structured series 3\n",
      "multidimensional index 2\n",
      "origin 2\n",
      "epistemology 1\n",
      "hierar 1\n",
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      "meas 1\n",
      "metadata 2\n",
      "web resource space intrinsic characteristics 3\n",
      "view size 4\n",
      "specific multidimensional access method 3\n",
      "fuhr 1\n",
      "existing resources 4\n",
      "inherent scalability 2\n",
      "specialization relationship 2\n",
      "semantic ability 4\n",
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      "low dimensional resource spaces 3\n",
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      "erations 1\n",
      "query language 4\n",
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      "publishing date 2\n",
      "chord 1\n",
      "generic classes 3\n",
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      "certain period 3\n",
      "computation complexities 2\n",
      "human resources 4\n",
      "poor definition 3\n",
      "associated list 4\n",
      "spiral 1\n",
      "rela tional database model 2\n",
      "conceptual classifications 2\n",
      "lineage 1\n",
      "reusable capability processes 2\n",
      "following update statement 3\n",
      "compari 1\n",
      "concept refinements 2\n",
      "anything 2\n",
      "programming languages 3\n",
      "resource space system 4\n",
      "unary operation disjoin 2\n",
      "issuing process 2\n",
      "two resource class hierarchies 3\n",
      "load balancing technique 2\n",
      "aci 1\n",
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      "multilayer tables 2\n",
      "probabilities 3\n",
      "rele vant 1\n",
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      "corresponding key 4\n",
      "prerequisite condition 2\n",
      "population 2\n",
      "theo 1\n",
      "matrix 3\n",
      "emphasis 2\n",
      "sln autonomy 1\n",
      "complete operation 3\n",
      "resource class set 4\n",
      "institute 2\n",
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      "node split 3\n",
      "ops ops 1\n",
      "capa bility 1\n",
      "dimensionality schema 2\n",
      "sufficient detail 3\n",
      "semantic relevancy 2\n",
      "initial peer 2\n",
      "many local semantic overlay grids 2\n",
      "probabilistic resource space 3\n",
      "new zone allocation 2\n",
      "global overlay semantic grid 2\n",
      "paper 6\n",
      "relational data 3\n",
      "sum 3\n",
      "magnitude 2\n",
      "car 2\n",
      "universe space 2\n",
      "class operations 3\n",
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      "differ ent 2\n",
      "point query operation 3\n",
      "bit string coordinates 2\n",
      "sequence 4\n",
      "impact factor 3\n",
      "key dimension 3\n",
      "modify command 2\n",
      "message neighbors 2\n",
      "gossip mech anisms 1\n",
      "basic structured rsm algorithm 2\n",
      "dey 1\n",
      "special effort 3\n",
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      "constraints 3\n",
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      "devel opment 1\n",
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      "machine 3\n",
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      "single binary tree 1\n",
      "important contribution 1\n",
      "binary search algorithm 1\n",
      "real applications 1\n",
      "simple semantics 1\n",
      "index information 1\n",
      "framework project gredia 1\n",
      "large community 1\n",
      "appropriate classification 1\n",
      "semantic basis 1\n",
      "cross 1\n",
      "classification points 1\n",
      "relevant resources 1\n",
      "promising model 1\n",
      "special design method 1\n",
      "design data dictionary 1\n",
      "operation set applications 1\n",
      "certain relations 1\n",
      "previous sections 1\n",
      "corresponding view 1\n",
      "corresponding resource space 1\n",
      "range search 1\n",
      "normalized information space 1\n",
      "certain rules 1\n",
      "multiattribute search 1\n",
      "category respectively 1\n",
      "model retrieval 1\n",
      "single resource 1\n",
      "details 1\n",
      "discussed domain 1\n",
      "resource space definition language rsdl 1\n",
      "research groups 1\n",
      "consistent knowledge 1\n",
      "given space dimension 1\n",
      "single resource space 1\n",
      "two phenomena 1\n",
      "version 1\n",
      "whole top 1\n",
      "content classification 1\n",
      "three conditions 1\n",
      "following three mechanisms 1\n",
      "following three clauses 1\n",
      "appropriate representation 1\n",
      "two operation languages 1\n",
      "original idea 1\n",
      "even resource space 1\n",
      "information retrieval techniques 1\n",
      "general definition 1\n",
      "information retrieval 1\n",
      "domain application 1\n",
      "general purpose query language 1\n",
      "good categories 1\n",
      "different query languages 1\n",
      "large networks 1\n",
      "corresponding components 1\n",
      "previous algorithms 1\n",
      "also format 1\n",
      "right hand 1\n",
      "function description 1\n",
      "general purpose 1\n",
      "range point 1\n",
      "increasing order 1\n",
      "mainly online data analysis 1\n",
      "parameters 1\n",
      "one community 1\n",
      "design method resource space model 1\n",
      "abstract data types 1\n",
      "common resources 1\n",
      "two operation sets 1\n",
      "output resources 1\n",
      "instance 1\n",
      "design process 1\n",
      "experiment 1\n",
      "three major differences 1\n",
      "probability point 1\n",
      "given resource space resource class set 1\n",
      "question 1\n",
      "three basic components 1\n",
      "semantic relation 1\n",
      "many levels 1\n",
      "two kinds 1\n",
      "main differences 1\n",
      "standard description languages 1\n",
      "general idea 1\n",
      "following constraints 1\n",
      "maximum size 1\n",
      "semantic content 1\n",
      "quality 1\n",
      "one area 1\n",
      "following six types 1\n",
      "common problems 1\n",
      "original space 1\n",
      "one relationship 1\n",
      "baseline 1\n",
      "two important relationships 1\n",
      "small range 1\n",
      "top 1\n",
      "comparison formulas 1\n",
      "important parts 1\n",
      "following issues 1\n",
      "accuracy 1\n",
      "higher probability 1\n",
      "semantic goal 1\n",
      "one object 1\n",
      "space structure 1\n",
      "also semantics 1\n",
      "two kinds respectively 1\n",
      "semantic space 1\n",
      "single key search problem 1\n",
      "several categories 1\n",
      "information loss 1\n",
      "exist three types 1\n",
      "existing classification standard 1\n",
      "existing examples 1\n",
      "several differences 1\n",
      "many components 1\n",
      "well design 1\n",
      "certain size 1\n",
      "two basic relations 1\n",
      "source point 1\n",
      "good 1\n",
      "entities one 1\n",
      "many traditional methods 1\n",
      "new resources 1\n",
      "knowledge classification 1\n",
      "previous works 1\n",
      "new application 1\n",
      "new relations 1\n",
      "one kind 1\n",
      "vector space model 1\n",
      "representation aspect 1\n",
      "feature 1\n",
      "current 1\n",
      "source 1\n",
      "different aspects 1\n",
      "many ways 1\n",
      "many large networks 1\n",
      "detailed information 1\n",
      "one category 1\n",
      "average value 1\n",
      "two aspects 1\n",
      "three categories 1\n",
      "resulting models 1\n",
      "semantic representation 1\n",
      "multiple classification methods 1\n",
      "domain knowledge 1\n",
      "existing methods 1\n",
      "instance level 1\n",
      "step 1\n",
      "one type classification relation 1\n",
      "higher classification level 1\n",
      "many applications 1\n",
      "knowledge representation 1\n",
      "complete set 1\n",
      "macromolecular structure 1\n",
      "semantic 1\n",
      "single semantic point 1\n",
      "statistical data 1\n",
      "ursln respectively 1\n",
      "web semantic data model 1\n",
      "one remaining problem 1\n",
      "processing 1\n",
      "various type 1\n",
      "one sample 1\n",
      "real number 1\n",
      "basic data type 1\n",
      "even linear order 1\n",
      "domain level 1\n",
      "domain specific knowledge 1\n",
      "score 1\n",
      "required information 1\n",
      "important feature 1\n",
      "various types 1\n",
      "good representation approach 1\n",
      "need 1\n",
      "general cases 1\n",
      "one one mapping 1\n",
      "tasks 1\n",
      "new domain 1\n",
      "test scores 1\n",
      "existing data structure 1\n",
      "general form 1\n",
      "three good urements 1\n",
      "second step 1\n",
      "different parameters 1\n",
      "set list 1\n",
      "given point 1\n",
      "new generation data model 1\n",
      "particular case 1\n",
      "total number 1\n",
      "following two parts 1\n",
      "one pair 1\n",
      "context 1\n",
      "many cases 1\n",
      "main task 1\n",
      "natural language processing 1\n",
      "evaluation 1\n",
      "average number 1\n",
      "one point 1\n",
      "important part 1\n",
      "following types 1\n",
      "defined set 1\n",
      "one step 1\n",
      "following form 1\n",
      "natural number 1\n",
      "possible case 1\n",
      "previous data models 1\n",
      "following method 1\n",
      "new 1\n",
      "information systems 1\n",
      "first form 1\n",
      "new edocument model 1\n",
      "many data models 1\n",
      "data models 1\n",
      "thus data model 1\n",
      "future use 1\n",
      "different models 1\n",
      "two models 1\n",
      "data model 1\n",
      "second one 1\n",
      "cooresponding number 1\n",
      "data warehousing 1\n",
      "non1nf data 1\n",
      "different number 1\n",
      "different epistemologies 1\n",
      "using 1\n",
      "introduction 1\n",
      "one mbrs 1\n"
     ]
    }
   ],
   "source": [
    "print(len(action_link_counter))\n",
    "for word, inds in sorted(tf_link_counter.items(), key = lambda x: -len(x[1]) * get_idf_value(x[0])):\n",
    "    print(word, len(inds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compound concepts: find the abstract relations\n",
    "\n",
    "for pre, ind, rtype, post in triplets:\n",
    "    if rtype != ConstraintType:\n",
    "        continue\n",
    "\n",
    "    if pre in core_concepts:\n",
    "        if get_idf_value(post) > :\n",
    "            pass\n",
    "    \n",
    "    if post in core_concepts:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dependency chain\n",
    "\n",
    "1. 一个语义链内，前面的词依赖于后面的词\n",
    "2. 一句话内，前面的词依赖于后面的词，依赖强度取决于间隔了多少个语义节点\n",
    "\n",
    "- [ ] 是否考虑constraint link\n",
    "- [x] 使用idf阈值硬过滤效果不好，把idf软融入到dependency chain中\n",
    "\n",
    "$$\n",
    "Dependency(Pre, Post) = \\frac{}{}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('coordinates', 'axis') 174.5541710193902\n",
      "('resource space model', 'semantic link network') 124.92877236532084\n",
      "('node', 'sln') 114.02327536406798\n",
      "('resources', 'resource space') 102.04500295363385\n",
      "('alpha expression', 'axes') 92.66888188510747\n",
      "('rsm', 'sln') 90.19767934870063\n",
      "('peer', 'peers') 82.33344673948027\n",
      "('plications', 'resources') 80.95392934870064\n",
      "('resource space', 'axis') 78.22188188510746\n",
      "('coordinate', 'axis') 76.17609710354823\n"
     ]
    }
   ],
   "source": [
    "def get_dependency_matrix(link_list):\n",
    "    dependency_matrix = {}\n",
    "    position_mapper = {}\n",
    "\n",
    "    for link in link_list:\n",
    "        position = link.position\n",
    "        position_mapper[position] = position_mapper.get(position, []) + [link]\n",
    "\n",
    "    for section, links in position_mapper.items():\n",
    "        pre_list = []\n",
    "        # TODO: relation type给一个系数\n",
    "        for pre, ind, rtype, post, position in links:\n",
    "            # 离的越近，（_pre, post）后面的加权越多，所以pre_list需要逆转\n",
    "            for index, _pre in enumerate(pre_list, 1):\n",
    "                dependency_matrix[(_pre, post)] = dependency_matrix.get((_pre, post), 0) + (1 / index) * (get_idf_value(_pre) + get_idf_value(post)) / 2\n",
    "\n",
    "            # TODO: 更完善的计算\n",
    "            dependency_matrix[(pre, post)] = dependency_matrix.get((pre, post), 0) + (get_idf_value(pre) + get_idf_value(post) + get_idf_value(ind)) / 3\n",
    "            pre_list.append(pre)\n",
    "\n",
    "    return dependency_matrix\n",
    "\n",
    "main_dependency_matrix = get_dependency_matrix(main_links)\n",
    "\n",
    "# TODO: 删除反向依赖值\n",
    "core_dependency_matrix = {}\n",
    "for pair, value in main_dependency_matrix.items():\n",
    "    if pair[0] not in core_concepts or pair[1] not in core_concepts or pair[0] == pair[1]:\n",
    "        continue\n",
    "    core_dependency_matrix[pair] = value\n",
    "\n",
    "for pair, value in sorted(core_dependency_matrix.items(), key=lambda x: -x[1])[:10]:\n",
    "    print(pair, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct Dependency Chain\n",
    "\n",
    "# dependency chain\n",
    "class DependencyNode:\n",
    "\n",
    "    def __init__(self, content: str):\n",
    "        self.content = content\n",
    "        self.next = []\n",
    "        self.pre = []\n",
    "    \n",
    "    def __hash__(self):\n",
    "        return hash(self.content)\n",
    "    \n",
    "    def __eq__(self, query):\n",
    "        return self.content == query.content\n",
    "\n",
    "    def has_next(self, query):\n",
    "        # query is a chainNode\n",
    "        searched = set()\n",
    "        pending = {self}\n",
    "        \n",
    "        while pending:\n",
    "            node = pending.pop()\n",
    "            searched.add(node)\n",
    "            \n",
    "            if node == query:\n",
    "                return True\n",
    "            for next in node.next:\n",
    "                if next not in searched:\n",
    "                    pending.add(next)\n",
    "        \n",
    "        return False\n",
    "\n",
    "    def __str__(self):\n",
    "        return self.content\n",
    "        # string = f'{self.content}'\n",
    "        # strings = []\n",
    "        # for node in self.next:\n",
    "        #     strings.append(\n",
    "        #         f'{string} ----> {str(node)}'\n",
    "        #     )\n",
    "        # if not strings:\n",
    "        #     return string\n",
    "        # return '\\n'.join(strings)\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return f'ChainNode({self.content})'\n",
    "\n",
    "\n",
    "class DependencyForest:\n",
    "\n",
    "    def __init__(self):\n",
    "        self.roots = []\n",
    "        self.string_to_node = {}\n",
    "\n",
    "    def _append(self, pre_node, post_node):\n",
    "        # 如果存在post_node -> ... -> pre_node的依赖链路，则不添加\n",
    "        if post_node.has_next(pre_node):\n",
    "            return False\n",
    "        \n",
    "        return True\n",
    "\n",
    "    def append(self, pair):\n",
    "        # pair: (pre, post), where pre is depended on post\n",
    "        pre, post = pair\n",
    "        pre_node = self.search(pre)\n",
    "        post_node = self.search(post)\n",
    "\n",
    "        pre_as_root = False\n",
    "\n",
    "        if pre_node is not None and post_node is not None:\n",
    "            pass\n",
    "        elif pre_node is not None and post_node is None:\n",
    "            post_node = DependencyNode(post)\n",
    "        elif pre_node is None and post_node is not None:\n",
    "            pre_node = DependencyNode(pre)\n",
    "            pre_as_root = True\n",
    "        elif pre_node is None and post_node is None:\n",
    "            pre_node = DependencyNode(pre)\n",
    "            post_node = DependencyNode(post)\n",
    "            pre_as_root = True\n",
    "\n",
    "        # 如果存在post_node -> ... -> pre_node的依赖链路，则不添加\n",
    "        if post_node.has_next(pre_node):\n",
    "            return\n",
    "        \n",
    "        pre_node.next.append(post_node)\n",
    "        post_node.pre.append(pre_node)\n",
    "\n",
    "        # pre没有依赖，添加root节点\n",
    "        if pre_as_root:\n",
    "            self.roots.append(pre_node)\n",
    "\n",
    "        # post如果是root，移除\n",
    "        if post_node in self.roots:\n",
    "            self.roots.remove(post_node)\n",
    "        \n",
    "        self.string_to_node[pre_node.content] = pre_node\n",
    "        self.string_to_node[post_node.content] = post_node\n",
    "\n",
    "    def search(self, query):\n",
    "        return self.string_to_node.get(query, None)\n",
    "\n",
    "    def node_search(self, node, query):\n",
    "        # print('searching, ', node.content, query)\n",
    "        if node.content == query:\n",
    "            return node\n",
    "        for _node in node.next:\n",
    "            if (result := self.node_search(_node, query)):\n",
    "                return result\n",
    "\n",
    "    def get_node_sequence(self, from_concept, to_concept, forward=True) -> list[str]:\n",
    "        '''\n",
    "        forward: True, search process is from `from_node` to `to_node`\n",
    "            False, which is from `to_node` backward to `from_node`\n",
    "        \n",
    "        to_concept: if it is none, which represents the endless iteration\n",
    "        return the middle concepts from `from_node` to `to_node`\n",
    "        return [] if the dependency relations does not exist\n",
    "        \n",
    "        TODO: 遍历依赖强度顺序添加\n",
    "        notice that forward=True and forward=False may be inconsistent\n",
    "        '''\n",
    "        if (forward and not from_concept) or (not forward and not to_concept):\n",
    "            raise Exception('get node sequence invalid')\n",
    "        \n",
    "        from_node = self.string_to_node[from_concept] if from_concept else None\n",
    "        to_node = self.string_to_node[to_concept] if to_concept else None\n",
    "        \n",
    "        iterated_nodes = set()\n",
    "        paths = []\n",
    "        \n",
    "        if forward:\n",
    "            queue = [(from_node, [from_node])]\n",
    "            # 找到所有dependency sequence\n",
    "            while queue:\n",
    "                node, path = queue.pop()\n",
    "                iterated_nodes.add(node)\n",
    "                \n",
    "                for next in node.next:\n",
    "                    if next not in iterated_nodes:\n",
    "                        iterated_nodes.add(node)\n",
    "                        new_path = path + [next]\n",
    "                        if (not to_node and len(next.next) == 0) or (to_node and next == to_node):\n",
    "                            paths.append(new_path)\n",
    "                        else:\n",
    "                            queue.append(\n",
    "                                (next, new_path)\n",
    "                            )\n",
    "        \n",
    "        # 从后向前\n",
    "        if not forward:\n",
    "            queue = [(to_node, [to_node])]\n",
    "            # 找到所有dependency sequence\n",
    "            while queue:\n",
    "                node, path = queue.pop()\n",
    "                iterated_nodes.add(node)\n",
    "                \n",
    "                for pre in node.pre:\n",
    "                    if pre not in iterated_nodes:\n",
    "                        iterated_nodes.add(node)\n",
    "                        \n",
    "                        new_path = [pre] + path\n",
    "                        if (not from_node and len(pre.pre) == 0) or (from_node and pre == from_node):\n",
    "                            paths.append(new_path)\n",
    "                        else:\n",
    "                            queue.append(\n",
    "                                (pre, new_path)\n",
    "                            )\n",
    "        \n",
    "        # 按照path的长度，从长到短进行排序，生成摘要的时候可以动态选择，根据句子预算（sentence budget）\n",
    "        return sorted(paths, key=lambda p: len(p))\n",
    "\n",
    "    def __str__(self):\n",
    "        string = ''\n",
    "        for root in self.roots:\n",
    "            string += str(root) + '\\n==================\\n'\n",
    "        return string\n",
    "\n",
    "def construct_dependency_foreset(dependency_matrix):\n",
    "    dependency_forest = DependencyForest()\n",
    "    # 按照频繁集挖掘的策略来运行\n",
    "    for index, (pair, value) in enumerate(\n",
    "        tqdm.tqdm(sorted(dependency_matrix.items(), key=lambda x: -x[1]))\n",
    "    ):\n",
    "        # pair[0] 依赖 pair[1]\n",
    "        dependency_forest.append(pair)\n",
    "        \n",
    "    return dependency_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 693/693 [00:00<00:00, 94991.75it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(peer),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(peers),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(one peer),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(peer),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(one peer),\n",
       "  ChainNode(one),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(nodes),\n",
       "  ChainNode(semantic link),\n",
       "  ChainNode(semantic link network),\n",
       "  ChainNode(peer),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(example),\n",
       "  ChainNode(space),\n",
       "  ChainNode(coordinate),\n",
       "  ChainNode(introducer),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(nodes),\n",
       "  ChainNode(semantic link),\n",
       "  ChainNode(semantic link network),\n",
       "  ChainNode(peer),\n",
       "  ChainNode(query),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(nodes),\n",
       "  ChainNode(semantic link),\n",
       "  ChainNode(semantic link network),\n",
       "  ChainNode(peer),\n",
       "  ChainNode(peers),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(nodes),\n",
       "  ChainNode(semantic link),\n",
       "  ChainNode(semantic link network),\n",
       "  ChainNode(peer),\n",
       "  ChainNode(issuing message),\n",
       "  ChainNode(peers),\n",
       "  ChainNode(community)]]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_dependency_forest = construct_dependency_foreset(core_dependency_matrix)\n",
    "core_dependency_forest.roots[0].content\n",
    "core_dependency_forest.get_node_sequence('object', 'community')\n",
    "# print(core_dependency_forest.get_node_sequence('object', '', forward=True))\n",
    "# core_dependency_forest.get_node_sequence('', 'community', forward=False)\n",
    "# core_dependency_forest.get_node_sequence('community', '', forward=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 693/693 [00:00<00:00, 94411.69it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[ChainNode(object),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(one peer),\n",
       "  ChainNode(one),\n",
       "  ChainNode(community)],\n",
       " [ChainNode(object),\n",
       "  ChainNode(databases),\n",
       "  ChainNode(applications),\n",
       "  ChainNode(resources),\n",
       "  ChainNode(one peer),\n",
       "  ChainNode(one),\n",
       "  ChainNode(community)]]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_dependency_forest = construct_dependency_foreset(core_dependency_matrix)\n",
    "core_dependency_forest.get_node_sequence('object', 'community', forward=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51 / 101 concepts\n",
      "└──object\n",
      "   ├──databases\n",
      "   └──applications\n",
      "      ├──networks\n",
      "      ├──peer\n",
      "      │  ├──peers\n",
      "      │  ├──query\n",
      "      │  ├──network\n",
      "      │  ├──community\n",
      "      │  │  └──tree\n",
      "      │  └──issuing message\n",
      "      ├──files\n",
      "      ├──resource spaces\n",
      "      ├──operation\n",
      "      │  ├──ops\n",
      "      │  ├──disjoin\n",
      "      │  │  └──resource space\n",
      "      │  │     ├──two resource spaces\n",
      "      │  │     ├──axes\n",
      "      │  │     ├──sln\n",
      "      │  │     ├──semantic link network\n",
      "      │  │     │  └──redundancy\n",
      "      │  │     ├──cij\n",
      "      │  │     ├──designer\n",
      "      │  │     ├──semantic link\n",
      "      │  │     ├──rsn\n",
      "      │  │     └──space\n",
      "      │  ├──operations\n",
      "      │  ├──example\n",
      "      │  │  ├──point\n",
      "      │  │  ├──mbr\n",
      "      │  │  └──profes sor\n",
      "      │  ├──alpha expression\n",
      "      │  └──resource space calculus\n",
      "      │     ├──relational calculus\n",
      "      │     ├──coordinate\n",
      "      │     │  ├──introducer\n",
      "      │     │  └──nodes\n",
      "      │     └──axis\n",
      "      │        └──space dimension\n",
      "      ├──messages\n",
      "      │  └──gossip mechanisms\n",
      "      └──resources\n",
      "         ├──common axes\n",
      "         ├──node\n",
      "         │  ├──takeover timer\n",
      "         │  └──timer\n",
      "         ├──neighbors\n",
      "         └──one peer\n",
      "            ├──one\n",
      "            └──gossip\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def breath_first_iteration(root):\n",
    "    searched = set()\n",
    "    pending = {root}\n",
    "    mapper = {}\n",
    "        \n",
    "    while pending:\n",
    "        parent = pending.pop()\n",
    "        searched.add(parent)\n",
    "\n",
    "        mapper[parent.content] = []\n",
    "\n",
    "        for next in parent.next:\n",
    "            if next not in searched and next not in pending:\n",
    "                pending.add(next)\n",
    "                mapper[parent.content].append(next.content)\n",
    "\n",
    "    # print(mapper)\n",
    "    print(f'{len(searched)} / {len(core_concepts)} concepts')\n",
    "    # return len(searched), print_tree(root.content, mapper, '')\n",
    "    return print_tree(root.content, mapper, '')\n",
    "        \n",
    "def print_tree(root, mapper, string, last=True, header='') -> str:\n",
    "    elbow = \"└──\"\n",
    "    pipe = \"│  \"\n",
    "    tee = \"├──\"\n",
    "    blank = \"   \"\n",
    "    string += (header + (elbow if last else tee) + root) + '\\n'\n",
    "    children = mapper[root]\n",
    "    for i, c in enumerate(children):\n",
    "        string += print_tree(c, mapper, '', header=header + (blank if last else pipe), last=i == len(children) - 1)\n",
    "    return string\n",
    "\n",
    "# len(dependency_chain.chains[0])\n",
    "print(breath_first_iteration(dependency_forest.roots[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the history materials and build the dependency forest\n",
    "\n",
    "Merge the main dependency forest with the history dependency forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/71910 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 71910/71910 [00:31<00:00, 2277.81it/s]\n"
     ]
    }
   ],
   "source": [
    "history_links = load_links(Path('foundations_of_database.triplets'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 87128/87128 [01:15<00:00, 1153.50it/s]\n"
     ]
    }
   ],
   "source": [
    "history_dependency_matrix = get_dependency_matrix(history_links)\n",
    "# select the top xxx concepts\n",
    "history_dependency_forest = construct_dependency_foreset(history_dependency_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history_dependency_forest.roots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 88946/88946 [00:00<00:00, 718005.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3443\n",
      "databases -> resolution\n",
      "resolution -> head\n",
      "head -> oid\n",
      "oid -> form\n",
      "form -> semantic information\n",
      "semantic information -> designer\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3443/3443 [00:00<00:00, 7298.74it/s] \n"
     ]
    }
   ],
   "source": [
    "def extract_common_forest(main_foreset, history_forest):\n",
    "    '''\n",
    "    merge the side_chain to main_chain\n",
    "    '''\n",
    "    pairs = []\n",
    "    iterated_nodes = set()\n",
    "    queue = [(root, []) for root in history_forest.roots]\n",
    "    pair_sequences = []\n",
    "    # 找到所有dependency sequence\n",
    "    while queue:\n",
    "        node, pairs = queue.pop()\n",
    "        iterated_nodes.add(node)\n",
    "        for next in node.next:\n",
    "            if next not in iterated_nodes:\n",
    "                queue.append(\n",
    "                    (next, pairs + [(node, next)])\n",
    "                )\n",
    "            else:\n",
    "                pair_sequences.append(pairs)\n",
    "        if len(node.next) == 0:\n",
    "            pair_sequences.append(pairs)\n",
    "\n",
    "    filtered_pairs = []\n",
    "    # 找到最短路径\n",
    "    for sequence in tqdm.tqdm(pair_sequences):\n",
    "        start = -1; end = -1\n",
    "        for index, (pre, post) in enumerate(sequence):\n",
    "            if pre.content in main_foreset.string_to_node and start == -1:\n",
    "                start = index\n",
    "            if post.content in main_foreset.string_to_node and start != -1:\n",
    "                end = index + 1\n",
    "\n",
    "        if end != -1:\n",
    "            # 获取中间所有concepts\n",
    "            filtered_pairs.append(sequence[start:end])\n",
    "\n",
    "    print(len(filtered_pairs))\n",
    "    \n",
    "    for pre, post in filtered_pairs[0]:\n",
    "        print(pre.content, '->', post.content)\n",
    "\n",
    "    # for filtered_pair in filtered_pairs:\n",
    "    #     main_dependency_matrix.append(filtered_pair)\n",
    "    \n",
    "    common_forest = DependencyForest()\n",
    "    for pairs in tqdm.tqdm(filtered_pairs):\n",
    "        for pre, post in pairs:\n",
    "            common_forest.append((pre.content, post.content))\n",
    "    return common_forest\n",
    "\n",
    "common_history_forest = extract_common_forest(core_dependency_forest, history_dependency_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'designer' in core_dependency_forest.string_to_node"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Decompose / 解耦 dependency tree\n",
    "\n",
    "* 把core concepts按条目分为多条学习路径\n",
    "* 把core concepts的不同路径，直接忽视掉某些不重要的节点\n",
    "\n",
    "$$\n",
    "Importance(D) = \\alpha * Importance(D) + \\beta * \\sum_{T_i \\in Incoming}\\frac{PR(T_i)}{|Incoming(D)|} + (1 - \\beta) * \\sum_{T_i \\in Outgoing}\\frac{PR(T_i)}{|Outgoing(D)|} $$\n",
    "$$\n",
    "\\alpha = \\frac{|Outgoing(D)| + |Incoming(D)|}{N}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('resource space', 0.03009720930540835),\n",
       " ('axis', 0.024989215630144106),\n",
       " ('semantic link network', 0.020338168970456684),\n",
       " ('sln', 0.017983957288784193),\n",
       " ('rsn', 0.017254492358874565),\n",
       " ('peer', 0.01716773903677528),\n",
       " ('designer', 0.015230931568747915),\n",
       " ('query', 0.015225757572200911),\n",
       " ('relational calculus', 0.015100259730818903),\n",
       " ('axes', 0.014806191779589748),\n",
       " ('two resource spaces', 0.014596285155379637),\n",
       " ('network', 0.01443309836567343),\n",
       " ('resource spaces', 0.014203950314280185),\n",
       " ('nodes', 0.013823117549164116),\n",
       " ('semantic link', 0.013599009098811234),\n",
       " ('peers', 0.013232022714070948),\n",
       " ('gossip mechanisms', 0.013176542512581775),\n",
       " ('resources', 0.012819400278791804),\n",
       " ('coordinate', 0.012567199841158526),\n",
       " ('cij', 0.01092935661906289),\n",
       " ('networks', 0.009870856181365212),\n",
       " ('community', 0.009146195106133686),\n",
       " ('common axes', 0.008662267451541993),\n",
       " ('redundancy', 0.008404179767541846),\n",
       " ('tree', 0.007548555738884385),\n",
       " ('space', 0.007109793256949995),\n",
       " ('example', 0.007075264291391638),\n",
       " ('operations', 0.006343923133390628),\n",
       " ('messages', 0.006256518377101811),\n",
       " ('issuing message', 0.005573660184514094),\n",
       " ('node', 0.005525017574021308),\n",
       " ('point', 0.005480026124309878),\n",
       " ('space dimension', 0.005449841301310282),\n",
       " ('neighbors', 0.005437989706003704),\n",
       " ('introducer', 0.005174084584869105),\n",
       " ('files', 0.004578609796712381),\n",
       " ('alpha expression', 0.004540856589834533),\n",
       " ('resource space calculus', 0.004534011788570146),\n",
       " ('ops', 0.004316662056910491),\n",
       " ('one', 0.004107026090517458),\n",
       " ('resource space model', 0.003814139453954083),\n",
       " ('profes sor', 0.0036168059243466013),\n",
       " ('operation', 0.0035870027811108116),\n",
       " ('disjoin', 0.003292658207893812),\n",
       " ('one peer', 0.0029883721567205432),\n",
       " ('takeover timer', 0.0028235448757167082),\n",
       " ('mbr', 0.0025241660160696348),\n",
       " ('ddl', 0.00250682213253579),\n",
       " ('gossip', 0.0024681896006158574),\n",
       " ('databases', 0.0020740352908652127),\n",
       " ('relational database model', 0.0020562208849521507),\n",
       " ('data warehouse', 0.0020039954238576705),\n",
       " ('storing', 0.0020039954238576705),\n",
       " ('timer', 0.0019248833861815577),\n",
       " ('applications', 0.0010189024787561085),\n",
       " ('resource', 8.605741712418195e-05),\n",
       " ('resource index', 6.325536026753167e-05),\n",
       " ('limit', 3.947366718117709e-05),\n",
       " ('coordinates', 3.550000414512842e-05),\n",
       " ('rsm', 2.1634311815245956e-05),\n",
       " ('concepts', 1.4083419693550997e-05),\n",
       " ('people', 1.320138127786412e-05),\n",
       " ('plications', 2.4654045509250747e-06),\n",
       " ('approach', 5.061759495830497e-07),\n",
       " ('new peer', 2.855858374329675e-07),\n",
       " ('users', 2.8522924546517634e-07),\n",
       " ('dimension', 1.4019452158216784e-07),\n",
       " ('theorem', 5.8925805147539125e-08),\n",
       " ('chapter', 3.5800702725767345e-08),\n",
       " ('probabilistic resource space model', 3.5781405736622297e-08),\n",
       " ('calculus', 2.278218022238252e-08),\n",
       " ('zone', 1.1042970982089173e-09),\n",
       " ('routing process', 7.083464868984262e-10),\n",
       " ('hierarchy semantics', 3.730614837072628e-10),\n",
       " ('new node', 2.9721403989939804e-10),\n",
       " ('rsm schema tree', 1.938066994678148e-10),\n",
       " ('resource indices', 1.938066994678148e-10),\n",
       " ('user', 5.826949614503468e-11),\n",
       " ('designers', 1.4551915228366852e-11),\n",
       " ('structured rsm', 1.4551915228366852e-11),\n",
       " ('readers', 3.785287098980758e-13),\n",
       " ('worst case', 3.785287098980758e-13),\n",
       " ('keywords', 3.785287098980758e-13),\n",
       " ('data cube', 2.842170943040401e-14),\n",
       " ('query initiator', 2.842170943040401e-14),\n",
       " ('rol', 2.842170943040401e-14),\n",
       " ('fig.2.6', 2.842170943040401e-14),\n",
       " ('object', 7.393138865196793e-16),\n",
       " ('ontology', 7.393138865196793e-16),\n",
       " ('quotient semantic link network', 7.393138865196793e-16),\n",
       " ('corre', 7.393138865196793e-16),\n",
       " ('dotted circles', 7.393138865196793e-16),\n",
       " ('customers', 7.941848390348116e-18),\n",
       " ('goods', 1.4439724346087486e-18),\n",
       " ('commands', 1.4439724346087486e-18),\n",
       " ('proof', 1.4439724346087486e-18)]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算所有节点的重要度\n",
    "importances = {}\n",
    "\n",
    "# importance init\n",
    "for concept in core_dependency_forest.string_to_node.keys():\n",
    "    importances[concept] = 1 # or the count of action links\n",
    "\n",
    "# importance transfer\n",
    "N = len(core_dependency_forest.string_to_node.keys())\n",
    "beta = 0.4\n",
    "\n",
    "# the ending condition: \n",
    "for i in range(9):\n",
    "    for concept in core_dependency_forest.string_to_node.keys():\n",
    "        incoming = [n.content for n in core_dependency_forest.string_to_node[concept].pre]\n",
    "        outgoing = [n.content for n in core_dependency_forest.string_to_node[concept].next]\n",
    "    \n",
    "        importances[concept] *= (len(incoming) +len(outgoing)) / N\n",
    "    \n",
    "        importances[concept] += beta * sum(importances[p] for p in incoming) / (len(incoming) + 1)\n",
    "        importances[concept] += (1 - beta) * sum(importances[p] for p in incoming) / (len(incoming) + 1)\n",
    "\n",
    "sorted(importances.items(), key=lambda x: -x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the importances of pairs\n",
    "# 入链可以重复，出链不能重复\n",
    "\n",
    "pair_importances = []\n",
    "for pre, post, dependency_coef in pairs:\n",
    "    pair_importances.append(\n",
    "        pair, (importances[pre] + importances[post]) * dependency_coef\n",
    "    )\n",
    "\n",
    "chains = []\n",
    "solved_nodes = []\n",
    "\n",
    "the_most_important_pair = sorted(pair_importances, key=lambda x: x[1])[0]\n",
    "\n",
    "while True:\n",
    "    chain = select_a_chain(the_most_important_pair, 0.3, all_pairs)\n",
    "    \n",
    "\n",
    "def select_a_chain(start_pair, threshold, all_pairs):\n",
    "    chain = DependencyChain()\n",
    "    query_pre, query_post = start_pair\n",
    "\n",
    "    for pre, post in all_pairs:\n",
    "        if post == query_pre and importance > threshold:\n",
    "            chain.append((pre, post))\n",
    "\n",
    "            # 所有包含pre节点的pair全部降权重\n",
    "            importance *= 0.5\n",
    "\n",
    "        if pre == query_post and importance > threshold:\n",
    "            chain.append((pre, post))\n",
    "\n",
    "            # 移除所有包含post节点的pair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 生成课程大纲\n",
    "\n",
    "### 推理句子选择：限制与规则\n",
    "\n",
    "输入：预期的句子数量\n",
    "\n",
    "\n",
    "4. 可读性\n",
    "\n",
    "分为三段；将句子数量按照某种固定比例分配在三段中。如10%，20%，70%；也可以按照$\\frac{|basis-concepts|}{|core-concepts|}$的比例计算\n",
    "    1) 只包含basis concepts; 2) 包含basis concepts和core conceptes; 3) 只包含core concepts\n",
    "\n",
    "\n",
    "1. 信息量\n",
    "\n",
    "ranking 句子的方法\n",
    "\n",
    "2. 连贯性\n",
    "\n",
    "所有句子必须遵循依赖关系，不能跳concepts，结合【位置】信息。\n",
    "\n",
    "3. 相关性\n",
    "\n",
    "title, section title作为指导信息\n",
    "\n",
    "5. 简洁性\n",
    "\n",
    "针对连贯性抽取整个句子，根据语义链，保留核心内容。即不直接抽取整个句子，抽取出包含该依赖关系的重要部分作为半句话，与其他半句进行连接。\n",
    "\n",
    "一页A4纸，多少句子/词：有应用背景的文本摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1 Resource Space Model Methodology: 100%|██████████| 2/2 [00:00<00:00, 3002.37it/s]\n",
      "1.1 Origin of the Resource Space Model: 100%|██████████| 30/30 [00:00<00:00, 4935.06it/s]\n",
      "1.2 Basis of the Resource Space Model: 100%|██████████| 1/1 [00:00<00:00, 17260.51it/s]\n",
      "1.2.1 Definitions and Characteristics: 100%|██████████| 38/38 [00:00<00:00, 6278.16it/s]\n",
      "1.2.2 Resource Space Definition Language: 100%|██████████| 6/6 [00:00<00:00, 5994.72it/s]\n",
      "1.2.3 Resource Space Manipulation Operations: 100%|██████████| 3/3 [00:00<00:00, 3076.51it/s]\n",
      "1.2.4 Resource Space Modification: 100%|██████████| 4/4 [00:00<00:00, 6859.04it/s]\n",
      "1.2.5 View Definition: 100%|██████████| 4/4 [00:00<00:00, 5909.55it/s]\n",
      "1.2.6 Query Language: 100%|██████████| 7/7 [00:00<00:00, 6611.15it/s]\n",
      "1.2.7 Visualized Resource Locating: 100%|██████████| 3/3 [00:00<00:00, 4232.40it/s]\n",
      "1.3 Application Scenarios of the Resource Space Model: 100%|██████████| 1/1 [00:00<00:00, 26214.40it/s]\n",
      "1.3.1 Management of Web Pages: 100%|██████████| 4/4 [00:00<00:00, 3933.70it/s]\n",
      "1.3.2 Managing Multi-layer Tables: 100%|██████████| 6/6 [00:00<00:00, 5395.76it/s]\n",
      "1.3.3 Management of Photos: 100%|██████████| 4/4 [00:00<00:00, 4641.00it/s]\n",
      "1.3.4 Geographical Resource Space: 100%|██████████| 3/3 [00:00<00:00, 7635.26it/s]\n",
      "1.3.5  Multi-dimensional ACM Computing Classification System: 100%|██████████| 3/3 [00:00<00:00, 2034.10it/s]\n",
      "1.3.6 Management of Bio-information: 100%|██████████| 1/1 [00:00<00:00, 306.76it/s]\n",
      "1.3.7 Media Content Space: 100%|██████████| 1/1 [00:00<00:00, 2187.95it/s]\n",
      "1.3.8 Automatically Add New Resources to the Resource Space: 100%|██████████| 17/17 [00:00<00:00, 7830.35it/s]\n",
      "1.4 Design Method: 100%|██████████| 11/11 [00:00<00:00, 16465.86it/s]\n",
      "1.4.1 Resource Analysis: 100%|██████████| 10/10 [00:00<00:00, 11722.48it/s]\n",
      "1.4.2 Top-down Resource Partition: 100%|██████████| 8/8 [00:00<00:00, 8869.79it/s]\n",
      "1.4.3 From Low Dimension to High Dimension: 100%|██████████| 17/17 [00:00<00:00, 7617.05it/s]\n",
      "1.4.4 Abstraction and Analogy in Designing Resource Space: 100%|██████████| 20/20 [00:00<00:00, 7757.17it/s]\n",
      "1.5 Use Resource Space to Manage Relational Tables: 100%|██████████| 8/8 [00:00<00:00, 7745.71it/s]\n",
      "1.6 The Semantic Link Network: 100%|██████████| 24/24 [00:00<00:00, 7117.53it/s]\n",
      "1.7 Comparison between RSM and RDBM: 100%|██████████| 16/16 [00:00<00:00, 4052.71it/s]\n",
      "1.8 Questions and Answers: 100%|██████████| 20/20 [00:00<00:00, 6865.78it/s]\n",
      "1.9 Summary: 100%|██████████| 13/13 [00:00<00:00, 7387.34it/s]\n",
      "2 A Semantic Overlay Integrating Normalization with Autonomy: 100%|██████████| 1/1 [00:00<00:00, 4609.13it/s]\n",
      "2.1 The Basic Idea: 100%|██████████| 12/12 [00:00<00:00, 4767.61it/s]\n",
      "2.2 Integrating Resource Space Model with Semantic Link Network: 100%|██████████| 14/14 [00:00<00:00, 4642.28it/s]\n",
      "2.3 Relationship between RSM and SLN: 100%|██████████| 1/1 [00:00<00:00, 32263.88it/s]\n",
      "2.3.1 Transformation from Semantic Link Network to Resource Space Model: 100%|██████████| 19/19 [00:00<00:00, 5824.57it/s]\n",
      "2.3.2 Transformation from Resource Space to Semantic Link Network and Correlations: 100%|██████████| 14/14 [00:00<00:00, 4826.19it/s]\n",
      "2.3.3 Topological Properties: 100%|██████████| 17/17 [00:00<00:00, 2747.18it/s]\n",
      "2.4 Union View of Resource Space and Semantic Link Network: 100%|██████████| 1/1 [00:00<00:00, 27776.85it/s]\n",
      "2.4.1 The Framework: 100%|██████████| 9/9 [00:00<00:00, 3921.54it/s]\n",
      "2.4.2 The Core Component of Union View Resource Class Hierarchy: 100%|██████████| 15/15 [00:00<00:00, 4143.48it/s]\n",
      "2.4.3 Operations on Resource Class Hierarchy: 100%|██████████| 35/35 [00:00<00:00, 5127.51it/s]\n",
      "2.4.4. Operations on the Union View of Resource Space and Semantic Link Network: 100%|██████████| 9/9 [00:00<00:00, 3199.59it/s]\n",
      "2.5 Discussion and Summary: 100%|██████████| 12/12 [00:00<00:00, 7037.42it/s]\n",
      "3 Expressiveness of Query Languages for Resource Space Model: 100%|██████████| 1/1 [00:00<00:00, 2993.79it/s]\n",
      "3.1 The Problem: 100%|██████████| 3/3 [00:00<00:00, 2390.37it/s]\n",
      "3.2 Completeness of Query Languages on Resource Spaces: 100%|██████████| 1/1 [00:00<00:00, 2015.52it/s]\n",
      "3.2.1 Basic Idea: 100%|██████████| 1/1 [00:00<00:00, 3688.92it/s]\n",
      "3.2.2 Definition of Completeness of Query Operations: 100%|██████████| 7/7 [00:00<00:00, 5736.64it/s]\n",
      "3.3 Complete set of Operations: 100%|██████████| 1/1 [00:00<00:00, 5777.28it/s]\n",
      "3.3.1 Design of Query Operations: 100%|██████████| 15/15 [00:00<00:00, 5141.76it/s]\n",
      "3.3.2 Verification of Completeness of Operations: 100%|██████████| 34/34 [00:00<00:00, 6567.48it/s]\n",
      "3.4 Expressiveness of Query Languages: 100%|██████████| 1/1 [00:00<00:00, 3313.04it/s]\n",
      "3.4.1 Comparison between Expressiveness: 100%|██████████| 6/6 [00:00<00:00, 3840.94it/s]\n",
      "3.4.2 Some Characteristics of Expressiveness: 100%|██████████| 10/10 [00:00<00:00, 13160.67it/s]\n",
      "3.5 Comparison and Analysis: 100%|██████████| 3/3 [00:00<00:00, 2780.75it/s]\n",
      "3.6 Summary: 100%|██████████| 6/6 [00:00<00:00, 7861.86it/s]\n",
      "4 Algebra and Calculus of the Resource Space Model: 100%|██████████| 1/1 [00:00<00:00, 5882.61it/s]\n",
      "4.1 Basic Idea: 100%|██████████| 4/4 [00:00<00:00, 4048.56it/s]\n",
      "4.2 Resource Space Algebra: 100%|██████████| 3/3 [00:00<00:00, 3473.06it/s]\n",
      "4.2.1 Definitions of Operations: 100%|██████████| 23/23 [00:00<00:00, 3926.93it/s]\n",
      "4.2.2 Relationships among Operations: 100%|██████████| 15/15 [00:00<00:00, 2783.95it/s]\n",
      "4.3 Resource Space Calculus: 100%|██████████| 1/1 [00:00<00:00, 7345.54it/s]\n",
      "4.3.1 Definition: 100%|██████████| 37/37 [00:00<00:00, 10818.35it/s]\n",
      "4.3.2 From Resource Space Algebra to Resource Space Calculus: 100%|██████████| 11/11 [00:00<00:00, 3739.15it/s]\n",
      "4.3.3 From Resource Space Calculus to Resource Space Algebra: 100%|██████████| 37/37 [00:00<00:00, 6599.02it/s]\n",
      "4.3.4 Transformation from Relational Model to Resource Space: 100%|██████████| 6/6 [00:00<00:00, 3275.09it/s]\n",
      "4.4 Summary: 100%|██████████| 1/1 [00:00<00:00, 2403.61it/s]\n",
      "5 Searching Complexity of Resource Space Model: 100%|██████████| 1/1 [00:00<00:00, 3521.67it/s]\n",
      "5.1 Basic Concepts and Formulas: 100%|██████████| 1/1 [00:00<00:00, 3715.06it/s]\n",
      "5.1.1 On Computation Complexity: 100%|██████████| 15/15 [00:00<00:00, 4840.70it/s]\n",
      "5.1.2 Searching Complexity and Formulas: 100%|██████████| 11/11 [00:00<00:00, 9909.22it/s]\n",
      "5.2 Basic Assumptions: 100%|██████████| 6/6 [00:00<00:00, 3627.77it/s]\n",
      "5.3 Distribution of Coordinates on Axes: 100%|██████████| 1/1 [00:00<00:00, 2347.12it/s]\n",
      "5.3.1 Best Distribution of Coordinates: 100%|██████████| 7/7 [00:00<00:00, 3291.13it/s]\n",
      "5.3.2 The Worst Distribution of Coordinates: 100%|██████████| 20/20 [00:00<00:00, 5766.95it/s]\n",
      "5.4 The Changing of Space Dimension: 100%|██████████| 1/1 [00:00<00:00, 3457.79it/s]\n",
      "5.4.1 Relationship between Dimension and Searching Complexity: 100%|██████████| 7/7 [00:00<00:00, 3884.64it/s]\n",
      "5.4.2 Value of Critical Dimension: 100%|██████████| 8/8 [00:00<00:00, 5337.11it/s]\n",
      "5.5 Summary: 100%|██████████| 1/1 [00:00<00:00, 2434.30it/s]\n",
      "6 Resource Space Model Storage: 100%|██████████| 1/1 [00:00<00:00, 5817.34it/s]\n",
      "6.1 Current Approaches to Storing Resource Space: 100%|██████████| 7/7 [00:00<00:00, 3078.23it/s]\n",
      "6.2 Problem Definition: 100%|██████████| 7/7 [00:00<00:00, 4834.53it/s]\n",
      "6.3 System Architecture: 100%|██████████| 6/6 [00:00<00:00, 3450.21it/s]\n",
      "6.4 RSM Storage Mechanism: 100%|██████████| 10/10 [00:00<00:00, 8331.95it/s]\n",
      "6.5 RSM Schema Tree: 100%|██████████| 44/44 [00:00<00:00, 4399.17it/s]\n",
      "6.6 C-tree: 100%|██████████| 4/4 [00:00<00:00, 4224.93it/s]\n",
      "6.6.1 Resource Operations: 100%|██████████| 3/3 [00:00<00:00, 3703.03it/s]\n",
      "6.6.2 Minimum Bounding Rectangle: 100%|██████████| 9/9 [00:00<00:00, 4810.59it/s]\n",
      "6.6.3 On INSERT_POLICY: 100%|██████████| 12/12 [00:00<00:00, 6761.37it/s]\n",
      "6.6.4 On SPLIT_POLICY: 100%|██████████| 5/5 [00:00<00:00, 4925.20it/s]\n",
      "6.6.5 Disk management: 100%|██████████| 2/2 [00:00<00:00, 2462.89it/s]\n",
      "6.7 Summary: 100%|██████████| 2/2 [00:00<00:00, 2914.74it/s]\n",
      "7 Structured Peer-to-Peer Resource Space: 100%|██████████| 1/1 [00:00<00:00, 6204.59it/s]\n",
      "7.1 Basic Idea: 100%|██████████| 1/1 [00:00<00:00, 24818.37it/s]\n",
      "7.1.1 The Problem: 100%|██████████| 8/8 [00:00<00:00, 7641.64it/s]\n",
      "7.1.2 A Brief Introduction to CAN: 100%|██████████| 3/3 [00:00<00:00, 4183.15it/s]\n",
      "7.1.3 Basic Approach: 100%|██████████| 5/5 [00:00<00:00, 5726.79it/s]\n",
      "7.2 The System Design: 100%|██████████| 1/1 [00:00<00:00, 30174.85it/s]\n",
      "7.2.1 The Basis: 100%|██████████| 7/7 [00:00<00:00, 4703.64it/s]\n",
      "7.2.2 Node State: 100%|██████████| 4/4 [00:00<00:00, 5998.29it/s]\n",
      "7.2.3 Routing: 100%|██████████| 21/21 [00:00<00:00, 7823.80it/s]\n",
      "7.2.4 Node Join: 100%|██████████| 7/7 [00:00<00:00, 2676.89it/s]\n",
      "7.2.5 Node Departure: 100%|██████████| 1/1 [00:00<00:00, 629.78it/s]\n",
      "7.3 Improvement: 100%|██████████| 3/3 [00:00<00:00, 10913.19it/s]\n",
      "7.3.1 Routing Performance: 100%|██████████| 12/12 [00:00<00:00, 5667.98it/s]\n",
      "7.3.2 Node Failure Recovery: 100%|██████████| 3/3 [00:00<00:00, 2873.47it/s]\n",
      "7.3.3 Coordinates in Tree Structure: 100%|██████████| 9/9 [00:00<00:00, 5195.97it/s]\n",
      "7.4 Summary: 100%|██████████| 4/4 [00:00<00:00, 6172.63it/s]\n",
      "8 Unstructured Peer-to-Peer Resource Space: 100%|██████████| 1/1 [00:00<00:00, 7810.62it/s]\n",
      "8.1 Unstructured Peer-to-Peer: 100%|██████████| 10/10 [00:00<00:00, 5999.58it/s]\n",
      "8.2 Incorporating Resource Space with Unstructured Peer-to-Peer: 100%|██████████| 1/1 [00:00<00:00, 34100.03it/s]\n",
      "8.2.1 Peer-to-Peer in e-Science: 100%|██████████| 10/10 [00:00<00:00, 5790.84it/s]\n",
      "8.2.2 Integrating Resource Space with Gossip: 100%|██████████| 5/5 [00:00<00:00, 4176.76it/s]\n",
      "8.3 The Construction Mechanism: 100%|██████████| 16/16 [00:00<00:00, 8104.94it/s]\n",
      "8.3.1 Resource Index Issuing Process: 100%|██████████| 1/1 [00:00<00:00, 1586.95it/s]\n",
      "8.3.2 Peer Join Process: 100%|██████████| 4/4 [00:00<00:00, 3675.99it/s]\n",
      "8.3.3 Peer Departure Process: 100%|██████████| 7/7 [00:00<00:00, 5651.61it/s]\n",
      "8.3.4 Query Processing Process: 100%|██████████| 5/5 [00:00<00:00, 5827.04it/s]\n",
      "8.4 Performance Analysis: 100%|██████████| 1/1 [00:00<00:00, 38479.85it/s]\n",
      "8.4.1 Reliability: 100%|██████████| 12/12 [00:00<00:00, 5856.60it/s]\n",
      "8.4.2 Hop Count Expectation: 100%|██████████| 7/7 [00:00<00:00, 11988.62it/s]\n",
      "8.5 Experimental Evaluation: 100%|██████████| 5/5 [00:00<00:00, 6403.52it/s]\n",
      "8.5.1 Experiments in Random Networks: 100%|██████████| 2/2 [00:00<00:00, 6100.81it/s]\n",
      "8.5.2 Experiments in Random Power-law Networks: 100%|██████████| 8/8 [00:00<00:00, 3055.68it/s]\n",
      "8.6 Architecture of a RSM-based Gossip Network: 100%|██████████| 9/9 [00:00<00:00, 8955.81it/s]\n",
      "8.7 Summary: 100%|██████████| 2/2 [00:00<00:00, 2325.00it/s]\n",
      "9 Probabilistic Resource Space Model: 100%|██████████| 1/1 [00:00<00:00, 9258.95it/s]\n",
      "9.1 Basic Concepts: 100%|██████████| 16/16 [00:00<00:00, 5128.69it/s]\n",
      "9.2 Normal Forms of Probabilistic Resource Space: 100%|██████████| 1/1 [00:00<00:00, 32263.88it/s]\n",
      "9.2.1 The First Normal Form and Second Normal Form: 100%|██████████| 6/6 [00:00<00:00, 5268.12it/s]\n",
      "9.2.2 The Third Normal Form: 100%|██████████| 13/13 [00:00<00:00, 6313.80it/s]\n",
      "9.3 Operations of Probabilistic Resource Space: 100%|██████████| 1/1 [00:00<00:00, 32263.88it/s]\n",
      "9.3.1 Point Query: 100%|██████████| 4/4 [00:00<00:00, 4794.86it/s]\n",
      "9.3.2 Resource Query: 100%|██████████| 5/5 [00:00<00:00, 4825.48it/s]\n",
      "9.3.3 Resource Modification: 100%|██████████| 6/6 [00:00<00:00, 6786.90it/s]\n",
      "9.3.4 Operations on Probabilistic Resource Space: 100%|██████████| 15/15 [00:00<00:00, 5405.50it/s]\n",
      "9.4 Integrity Constraints under Probability: 100%|██████████| 1/1 [00:00<00:00, 5405.03it/s]\n",
      "9.4.1 Key in Probabilistic Resource Space Model: 100%|██████████| 8/8 [00:00<00:00, 4168.77it/s]\n",
      "9.4.2 Integrity Constraints in Probabilistic Resource Space Model: 100%|██████████| 18/18 [00:00<00:00, 5867.53it/s]\n",
      "9.5 Relevant Works: 100%|██████████| 4/4 [00:00<00:00, 2978.38it/s]\n",
      "9.6 Summary: 100%|██████████| 1/1 [00:00<00:00, 2202.89it/s]\n",
      "preface: 100%|██████████| 16/16 [00:00<00:00, 4428.46it/s]\n"
     ]
    }
   ],
   "source": [
    "# load rsm sentences\n",
    "\n",
    "position_section_mapper = {}\n",
    "\n",
    "for path in sorted(Path('rsm_coref/').glob('*')):\n",
    "    for para_index, paragraph in enumerate(tqdm.tqdm(path.read_text().split('\\n'), desc=path.name), 1):\n",
    "        for sent_index, sentence_text in enumerate(nltk.tokenize.sent_tokenize(paragraph), 1):\n",
    "            # sentence = Sentence(sentence_text)\n",
    "            words = nltk.word_tokenize(sentence_text)\n",
    "            position = f\"{path.name.split(' ')[0]}-{para_index}-{sent_index}\"\n",
    "            position_section_mapper[position] = (\n",
    "                sentence_text, words\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A second-normal-form resource space is a first-normal-form, and for any axis, any two coordinates are independent from each other.\n",
      "Chapter 9 constructs the probabilistic Resource Space Model to deal with uncertainty in applications by introducing the probability into the Resource Space Model.\n",
      "==============================\n",
      "1. resource space\n",
      "According to definition 3.1, an operation set OP on resource spac-es is complete if: for any given finite collection of resource spaces RS1, RS2, …, RSN in simple normal form, and an operation set OP on resource spac-es can get all the resource spaces in the form of: {RS(x1,  ,xd)| xk RSi (Xj), 1i N, d1 and 1k d }.\n",
      "So for any given fi-nite collection of resource spaces RS1, RS2, …, RSN in simple normal form, we can get that all the possible query results are in the form of {RS(x1,  , xd)| xk  RSi(Xj), 1iN, d1 and 1kd}, i.e., all the combi-nations of the coordinates of the resource spaces.\n",
      "------------------------------\n",
      "A resource space RS(X1, …, Xm, Y1, …, Yn, Z1, …, Zk) can be dis-joined into two resource spaces RS1(X1, …, Xm, Y1, …, Yn) and RS2(Y1, …, Yn, Z1, …, Zk) that store the same type of resources as that of A resource space RS(X1, …, Xm, Y1, …, Yn, Z1, …, Zk) such that two resource spaces RS1(X1, …, Xm, Y1, …, Yn) have n (1≤n≤min(|RS1|, |RS2|)) common axes and |RS|  n differ-ent axes, and |RS|=|RS1| + |RS2|  n. For any point p1 in A resource space RS(X1, …, Xm, Y1, …, Yn, Z1, …, Zk), there exists a set P of points in A resource space RS(X1, …, Xm, Y1, …, Yn, Z1, …, Zk), each element of which has the same projections on X1, …, Xm, Y1, …, Yn as any point p1 in RS1.\n",
      "The Union View of the Resource Space Model and the Semantic Link Network is a triple S = (VE, RE, RCH), where VE is a fi-nite set of nodes in the Union View which could include resources, ge-neric classes, printable classes and resource classes derived from the Resource Space Model; RE is a finite set of triple <v1, v2, re>, where RE rep-resents the relationship between nodes v1 and v2 coming from VE; and, RCH is a finite set of resource class hierarchies each of which corre-sponds to a resource space in the meaning of the Resource Space Model.\n",
      "==============================\n",
      "2. axis\n",
      "So classification is not only an approach to efficiently managing re-sources but also a basic method for human to know the real world.\n",
      "INSERT  R1…, Rm  INTO  RS1…, RSm  [AT  URSL1, … , URSLm] [WHERE  <conditional expression>]\n",
      "------------------------------\n",
      "For a Semantic Link Network SLN (N, SL), if there exists an equivalent relation R on the set of nodes N (SLN), then the quotient Semantic Link Network of a Semantic Link Network SLN (N, SL) is defined as SLN (N , SL), where N (SLN)= N (SLN)R={C1, C2, , Cm}, where Ci is an equivalent class in a Semantic Link Network SLN (N, SL) under an equivalent relation R; and, SL( Ci, Cj)={ SL(ni, nj) | ni, nj  a Semantic Link Network SLN (N, SL) and niCi, njCj }, 1 i, j m.\n",
      "Now we divide these m+1 resource spaces into two parts: RS1, RS2, …, RSm and RSm+1, for the preceding m spaces, from the assumption we can get that the nine operations can get the result spaces in the following form: {RS(x1,  , xd)| xk RSi (Xj), 1i m, d1 and 1k d }; for the re-source space RSm+1, as the above using operations Selection and Disjoin we can get: {RS(x1,  , xd)| xk RSm+1 (Xj) , 1j n and 1k d }.\n",
      "==============================\n",
      "3. semantic link network\n",
      "If resource spaces RS1, …, and RSn store the same type of resources and resource spaces RS1, …, and RSn have k ( k  [1, minimum(|RS1|, |RS2|)] ) common axes, then resource spaces RS1, …, and RSn can be joined into one resource space RS such that resource spaces RS1, …, and RSn RSn share k ( k  [1, minimum(|RS1|, |RS2|)] ) common axes and |RS| = |RS1| + |RS2| – k.  The join operation can be represented as follows:\n",
      "However if an attribute appears in only one of the resource spaces in the FROM clause, the resource_space qualifier can be omitted.\n",
      "------------------------------\n",
      "4.\tincrease dimensionality by joining resource spaces or adding a new dimension to the existing resource space according to application re-quirement;\n",
      "1.23, a Semantic Link Network usually consists of an abstraction level and an instance level (Zhuge, 2007).\n",
      "==============================\n",
      "4. sln\n",
      "MODIFY  SPACE  RS ADD  AXIS  Xi <Ci1, …, Cij>\n",
      "The X-axis gives the number of indices per node, while the Y-axis gives the percentage of nodes containing the cooresponding number of indices.\n",
      "------------------------------\n",
      "A Semantic Link Network can be established autonomously by incor-porating logical reasoning, analogical reasoning, inductive reasoning and assistant tools.\n",
      "Integration of The Resource Space Model and The Semantic Link Network can form a semantic overlay with the character-istics of normalization and autonomy.\n",
      "==============================\n",
      "5. rsn\n",
      "A Global Semantic Overlay Grid can be built by integrating the Re-source Space Model and Semantic Link Network as shown in Fig.2.3.\n",
      "The reason of considering power-law networks is that some unstruc-tured Peer-to-Peer networks are characterized by random power-law and heavy tailed degree distributions.\n",
      "------------------------------\n",
      "The axis X of a resource space RS at URSL will be separated into X1i (RS1), …, and Xnj(RSn).\n",
      "Then we can get for any giv-en finite collection of resource spaces RS1, RS2, …, RSN in simple normal form, the nine operations can get all the resource spaces in the form of: {RS(x1,  ,xd)| xk RSi(Xj), 1j n, d1 and 1k d}.\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "# 对句子进行信息量排序\n",
    "# 方案1: 直接用重要度排序\n",
    "# 方案2: PageRank类方法\n",
    "\n",
    "# 所有包含基础概念的句子，进行排序\n",
    "# TODO: 移除所有已经选过的句子，并降权\n",
    "# TODO: 输出加粗关键概念\n",
    "\n",
    "\n",
    "def get_importance_of_a_sentence(sentence, words, query_concepts):\n",
    "    # TODO: 用importances字典\n",
    "    if len(words) == 0:\n",
    "        return 0\n",
    "    score = sum(get_idf_value(concept) for word in words if word not in query_concepts)\n",
    "    score += sum(importances[concept] for word in words if word in query_concepts)\n",
    "    \n",
    "    return score / len(words)\n",
    "    \n",
    "global_selected_sentence = set()\n",
    "\n",
    "def summarize(links, concepts: set, sentence_count = 1):\n",
    "    # 过滤出所有包含指定concepts的句子子集\n",
    "    # TODO: concepts变为依赖关系，目前没有考虑【依赖关系】\n",
    "    filtered_sentences = []\n",
    "    selected_position = set()\n",
    "    for link in links:\n",
    "        if link.pre not in concepts and link.post not in concepts:\n",
    "            continue\n",
    "        if link.position in selected_position:\n",
    "            continue\n",
    "        selected_position.add(link.position)\n",
    "        sentence, words = position_section_mapper[link.position]\n",
    "        \n",
    "        # TODO: 句子不会重复选择，更好的策略\n",
    "        if sentence in global_selected_sentence:\n",
    "            continue\n",
    "        # global_selected_sentence.add(sentence)\n",
    "        \n",
    "        # TODO: sentence的评价标准没有加入和query concepts之间的关联，或者说get_importance_of_a_sentence的关联度不强\n",
    "        filtered_sentences.append((sentence, words, link))\n",
    "\n",
    "    # 按照position对所有句子进行排序 (sentence, words, link)\n",
    "    ranked_sentences = sorted(filtered_sentences, key=lambda item: -get_importance_of_a_sentence(item[0], item[1], concepts))\n",
    "    \n",
    "    result_sentences = []\n",
    "    for sentence, words, link in ranked_sentences[:sentence_count]:\n",
    "        result_sentences.append(sentence)\n",
    "        global_selected_sentence.add(sentence)\n",
    "    return result_sentences\n",
    "\n",
    "sentences = []\n",
    "\n",
    "# 算上roots以外，不在core concepts中的概念（sequence的结尾也是core concepts）\n",
    "basic_concepts = (set(common_history_forest.string_to_node.keys()) - set(core_concepts)) | set([root.content for root in common_history_forest.roots])\n",
    "\n",
    "# linking concepts是basic concepts和最核心core concepts之前存在依赖关系的concepts\n",
    "# TODO: 排序core concepts\n",
    "linking_concepts = set(common_history_forest.string_to_node.keys()) - set(basic_concepts)\n",
    "# 有多少条item，就有多少个链接路\n",
    "top_5_concepts = sorted(core_concepts, key=lambda c: -importances.get(c, 0))[:5]\n",
    "for query_concept in top_5_concepts:\n",
    "    linking_sequences = []\n",
    "    for from_concept in linking_concepts:\n",
    "        linking_sequence = core_dependency_forest.get_node_sequence(from_concept, query_concept, forward=False)\n",
    "        linking_sequences.append(linking_sequence)\n",
    "\n",
    "# rest_core_concepts是最最核心的概念，及之后的概念。\n",
    "# 暂时不考虑分点，直接用top5作为分点\n",
    "# rest_core_concepts = core_concepts - linking_concepts\n",
    "\n",
    "content_sequences = []\n",
    "for query_concept in top_5_concepts:\n",
    "    sequence = core_dependency_forest.get_node_sequence(query_concept, '', forward=True)\n",
    "    content_sequences.append(sequence)\n",
    "\n",
    "# 生成摘要\n",
    "summary = []\n",
    "background_sentences = summarize(main_links, basic_concepts, 2)\n",
    "summary += background_sentences\n",
    "summary.append('='*30)\n",
    "\n",
    "for index, (query_concept, linking_sequence, content_sequence) in enumerate(zip(top_5_concepts, linking_sequences, content_sequences), 1):\n",
    "    summary.append(f'{index}. {query_concept}')\n",
    "    # sequence转为concepts，逐个学习路径\n",
    "    linking_concepts = set()\n",
    "    for sequence in linking_sequence:\n",
    "        linking_concepts.update(node.content for node in sequence)\n",
    "    \n",
    "    motivation_sentences = summarize(main_links, linking_concepts, 2)\n",
    "    summary += motivation_sentences\n",
    "    summary.append('-'*30)\n",
    "    \n",
    "    # sequence转为concepts\n",
    "    for sequence in content_sequence:\n",
    "        content_concepts = set(node.content for node in sequence)\n",
    "    content_sentences = summarize(main_links, content_concepts, 2)\n",
    "    summary += content_sentences\n",
    "    summary.append('='*30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A second-normal-form \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m is a first-normal-form , and for any \u001b[31maxis\u001b[0m , any two \u001b[31mcoordinates\u001b[0m are independent from each other .\n",
      "Chapter 9 constructs the probabilistic Resource Space Model to deal with uncertainty in \u001b[31mapplications\u001b[0m by introducing the probability into the Resource Space Model .\n",
      "==============================\n",
      "1. \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m\n",
      "According to definition 3.1 , an \u001b[31moperation\u001b[0m set OP on \u001b[31mresource\u001b[0m spac-es is complete if : for any given finite collection of \u001b[31mresource\u001b[0m spaces RS1 , RS2 , … , RSN in simple normal form\u001b[32mform\u001b[0m , and an \u001b[31moperation\u001b[0m set OP on \u001b[31mresource\u001b[0m spac-es can get all the \u001b[31mresource\u001b[0m spaces in the form\u001b[32mform\u001b[0m of : { RS ( x1 ,  , xd ) | xk RSi ( Xj ) , 1i N , d1 and 1k d } .\n",
      "So for any given fi-nite collection of \u001b[31mresource\u001b[0m spaces RS1 , RS2 , … , RSN in simple normal form\u001b[32mform\u001b[0m , we can get that all the possible \u001b[31mquery\u001b[0m results are in the form\u001b[32mform\u001b[0m of { RS ( x1 ,  , xd ) | xk  RSi ( Xj ) , 1iN , d1 and 1kd } , i.e. , all the combi-nations of the \u001b[31mcoordinates\u001b[0m of the \u001b[31mresource\u001b[0m spaces .\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
      "A \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m RS ( X1 , … , Xm , Y1 , … , Yn , Z1 , … , Zk ) can be dis-joined into two \u001b[31mresource\u001b[0m spaces RS1 ( X1 , … , Xm , Y1 , … , Yn ) and RS2 ( Y1 , … , Yn , Z1 , … , Zk ) that store the same type of \u001b[31mresources\u001b[0m as that of A \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m RS ( X1 , … , Xm , Y1 , … , Yn , Z1 , … , Zk ) such that two \u001b[31mresource\u001b[0m spaces RS1 ( X1 , … , Xm , Y1 , … , Yn ) have n ( 1≤n≤min ( |RS1| , |RS2| ) ) common \u001b[31maxes\u001b[0m and |RS|  n differ-ent \u001b[31maxes\u001b[0m , and |RS|=|RS1| + |RS2|  n. For any \u001b[31mpoint\u001b[0m p1 in A \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m RS ( X1 , … , Xm , Y1 , … , Yn , Z1 , … , Zk ) , there exists a set P of points in A \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m RS ( X1 , … , Xm , Y1 , … , Yn , Z1 , … , Zk ) , each element of which has the same projections on X1 , … , Xm , Y1 , … , Yn as any \u001b[31mpoint\u001b[0m p1 in RS1 .\n",
      "The Union View of the Resource Space Model and the Semantic Link Network is a triple S = ( VE , RE , RCH ) , where VE is a fi-nite set of \u001b[31mnodes\u001b[0m in the Union View which could include \u001b[31mresources\u001b[0m , ge-neric classes , printable classes and \u001b[31mresource\u001b[0m classes derived from the Resource Space Model ; RE is a finite set of triple < v1 , v2 , re > , where RE rep-resents the relationship between \u001b[31mnodes\u001b[0m v1 and v2 coming from VE ; and , RCH is a finite set of \u001b[31mresource\u001b[0m class hierarchies each of which corre-sponds to a \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m in the meaning of the Resource Space Model .\n",
      "==============================\n",
      "2. \u001b[31maxis\u001b[0m\n",
      "So classification is not only an \u001b[31mapproach\u001b[0m to efficiently managing re-sources but also a basic method for human to know the real world .\n",
      "INSERT R1… , Rm INTO RS1… , RSm [ AT URSL1 , … , URSLm ] [ WHERE < conditional expression > ]\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
      "For a Semantic Link Network SLN ( N , SL ) , if there exists an equivalent relation R on the set of \u001b[31mnodes\u001b[0m N ( SLN ) , then the quotient Semantic Link Network of a Semantic Link Network SLN ( N , SL ) is defined as SLN  ( N  , SL ) , where N  ( SLN ) = N ( SLN ) R= { C1 , C2 ,  , Cm } , where Ci is an equivalent class in a Semantic Link Network SLN ( N , SL ) under an equivalent relation R ; and , SL ( Ci , Cj ) = { SL ( ni , nj ) | ni , nj  a Semantic Link Network SLN ( N , SL ) and niCi , njCj } , 1 i , j m .\n",
      "Now we divide these m+1 \u001b[31mresource\u001b[0m spaces into two parts : RS1 , RS2 , … , RSm and RSm+1 , for the preceding m spaces , from the assumption we can get that the nine \u001b[31moperations\u001b[0m can get the result spaces in the following form\u001b[32mform\u001b[0m : { RS ( x1 ,  , xd ) | xk RSi ( Xj ) , 1i m , d1 and 1k d } ; for the re-source \u001b[31mspace\u001b[0m RSm+1 , as the above using \u001b[31moperations\u001b[0m Selection and Disjoin we can get : { RS ( x1 ,  , xd ) | xk RSm+1 ( Xj ) , 1j n and 1k d } .\n",
      "==============================\n",
      "3. semantic link \u001b[31mnetwork\u001b[0m\n",
      "If \u001b[31mresource\u001b[0m spaces RS1 , … , and RSn store the same type of \u001b[31mresources\u001b[0m and \u001b[31mresource\u001b[0m spaces RS1 , … , and RSn have k ( k  [ 1 , minimum ( |RS1| , |RS2| ) ] ) common \u001b[31maxes\u001b[0m , then \u001b[31mresource\u001b[0m spaces RS1 , … , and RSn can be joined into \u001b[31mone\u001b[0m \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m RS such that \u001b[31mresource\u001b[0m spaces RS1 , … , and RSn RSn share k ( k  [ 1 , minimum ( |RS1| , |RS2| ) ] ) common \u001b[31maxes\u001b[0m and |RS| = |RS1| + |RS2| – k. The join \u001b[31moperation\u001b[0m can be represented as follows :\n",
      "However if an attribute appears in only \u001b[31mone\u001b[0m of the \u001b[31mresource\u001b[0m spaces in the FROM clause , the resource_space qualifier can be omitted .\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
      "4. increase dimensionality by joining \u001b[31mresource\u001b[0m spaces or adding a new \u001b[31mdimension\u001b[0m to the existing \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m according to application re-quirement ;\n",
      "1.23 , a Semantic Link Network usually consists of an abstraction level and an instance level ( Zhuge , 2007 ) .\n",
      "==============================\n",
      "4. \u001b[31msln\u001b[0m\n",
      "MODIFY SPACE RS ADD AXIS Xi < Ci1 , … , Cij >\n",
      "The X-axis gives the number of indices per \u001b[31mnode\u001b[0m , while the Y-axis gives the percentage of \u001b[31mnodes\u001b[0m containing the cooresponding number of indices .\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
      "A Semantic Link Network can be established autonomously by incor-porating logical reasoning , analogical reasoning , inductive reasoning and assistant tools .\n",
      "Integration of The Resource Space Model and The Semantic Link Network can form\u001b[32mform\u001b[0m a semantic overlay with the character-istics of normalization and autonomy .\n",
      "==============================\n",
      "5. \u001b[31mrsn\u001b[0m\n",
      "A Global Semantic Overlay Grid can be built by integrating the Re-source Space Model and Semantic Link Network as shown in Fig.2.3 .\n",
      "The reason of considering power-law \u001b[31mnetworks\u001b[0m is that some unstruc-tured Peer-to-Peer \u001b[31mnetworks\u001b[0m are characterized by random power-law and heavy tailed degree distributions .\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
      "The \u001b[31maxis\u001b[0m X of a \u001b[31mresource\u001b[0m \u001b[31mspace\u001b[0m RS at URSL will be separated into X1i ( RS1 ) , … , and Xnj ( RSn ) .\n",
      "Then we can get for any giv-en finite collection of \u001b[31mresource\u001b[0m spaces RS1 , RS2 , … , RSN in simple normal form\u001b[32mform\u001b[0m , the nine \u001b[31moperations\u001b[0m can get all the \u001b[31mresource\u001b[0m spaces in the form\u001b[32mform\u001b[0m of : { RS ( x1 ,  , xd ) | xk RSi ( Xj ) , 1j n , d1 and 1k d } .\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "import termcolor\n",
    "\n",
    "# TODO: 也可以作为评估指标，出现陌生概念的比例\n",
    "# 绿色：已经学会的概念，在history概念集中\n",
    "green_text = lambda s: termcolor.colored(s, 'green')\n",
    "# 蓝色：在【已学会-核心】概念之间的概念，common\n",
    "blue_text = lambda s: termcolor.colored(s, 'blue')\n",
    "# 红色：完全未知的核心概念\n",
    "red_text = lambda s: termcolor.colored(s, 'red')\n",
    "\n",
    "from termcolor import colored\n",
    "\n",
    "def colored_sentence(words: list[str]):\n",
    "    result = []\n",
    "    \n",
    "    for word in words:\n",
    "        if word in core_concepts:\n",
    "            word = red_text(word)\n",
    "            \n",
    "        if word in linking_concepts:\n",
    "            word += blue_text(word)\n",
    "\n",
    "        if word in basic_concepts:\n",
    "            word += green_text(word)\n",
    "            \n",
    "        result.append(word)\n",
    "        \n",
    "    return ' '.join(result)\n",
    "\n",
    "for sentence in summary:\n",
    "    print(colored_sentence(nltk.tokenize.word_tokenize(sentence)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Files are expanding in our daily-use PCs or laptops due to easier down-load from websites and email attachments.',\n",
       " ['Files',\n",
       "  'are',\n",
       "  'expanding',\n",
       "  'in',\n",
       "  'our',\n",
       "  'daily-use',\n",
       "  'PCs',\n",
       "  'or',\n",
       "  'laptops',\n",
       "  'due',\n",
       "  'to',\n",
       "  'easier',\n",
       "  'down-load',\n",
       "  'from',\n",
       "  'websites',\n",
       "  'and',\n",
       "  'email',\n",
       "  'attachments',\n",
       "  '.'])"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linking_concepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'databases': <__main__.DependencyNode at 0x1506bb3d0>,\n",
       " 'resolution': <__main__.DependencyNode at 0x150475390>,\n",
       " 'head': <__main__.DependencyNode at 0x150477450>,\n",
       " 'oid': <__main__.DependencyNode at 0x1500eb890>,\n",
       " 'form': <__main__.DependencyNode at 0x1500e9250>,\n",
       " 'semantic information': <__main__.DependencyNode at 0x150324650>,\n",
       " 'designer': <__main__.DependencyNode at 0x150327850>,\n",
       " 'exercise': <__main__.DependencyNode at 0x150324f90>,\n",
       " 'detail': <__main__.DependencyNode at 0x150327e50>,\n",
       " 'theorem': <__main__.DependencyNode at 0x1503268d0>,\n",
       " 'chapter': <__main__.DependencyNode at 0x150474b50>,\n",
       " 'ptime': <__main__.DependencyNode at 0x150325ed0>,\n",
       " 'space': <__main__.DependencyNode at 0x1503257d0>,\n",
       " 'part': <__main__.DependencyNode at 0x150324b50>,\n",
       " 'way': <__main__.DependencyNode at 0x150327810>,\n",
       " 'cell': <__main__.DependencyNode at 0x150324e90>,\n",
       " 'neighbors': <__main__.DependencyNode at 0x150325610>,\n",
       " 'example': <__main__.DependencyNode at 0x150325c50>,\n",
       " 'body': <__main__.DependencyNode at 0x150326150>,\n",
       " 'predicate': <__main__.DependencyNode at 0x1503273d0>,\n",
       " 'last supplementary relation': <__main__.DependencyNode at 0x150327b90>,\n",
       " 'rule': <__main__.DependencyNode at 0x1503271d0>,\n",
       " 'relational model': <__main__.DependencyNode at 0x150327710>,\n",
       " 'relational calculus': <__main__.DependencyNode at 0x150326cd0>,\n",
       " 'operations': <__main__.DependencyNode at 0x15038c990>,\n",
       " 'proof': <__main__.DependencyNode at 0x150325cd0>,\n",
       " 'hypergraphs': <__main__.DependencyNode at 0x150324850>,\n",
       " 'generalization': <__main__.DependencyNode at 0x150324490>,\n",
       " 'approach': <__main__.DependencyNode at 0x150325190>}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_history_forest.string_to_node"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评估课程大纲\n",
    "\n",
    "1. 用大模型对比，是否符合那几个准则\n",
    "2. ROUGE评分：用一组论文集合的abstract全部内容\n",
    "\n",
    "\n",
    "没有现成的数据集，我们首先用多文本摘要数据集进行评估，科技文档体裁，单词长度分类\n",
    "\n",
    "短文：合成数据集\n",
    "长文：多文档\n",
    "\n",
    "1、ROUGE\n",
    "\n",
    "多文档摘要\n",
    "\n",
    "    1） 合成数据集：Co\n",
    "    2） BigSurvey\n",
    "    3） 论文\n",
    "\n",
    "\n",
    "2、13B大模型评估多个维度指标\n",
    "    * 连贯性\n",
    "    * \n",
    "\n",
    "3、目标应用场景：\n",
    "论文集合，按照时间进行排序，前面的多少篇论文\n",
    "交叉混合相同位置的句子\n",
    "\n",
    "### 对比模型\n",
    "\n",
    "* 抽取式摘要\n",
    "    - kl\n",
    "    - textrank\n",
    "\n",
    "* 小模型\n",
    "Phi-1.5B\n",
    "Llama3-7B\n",
    "Llama3-13B\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m红色\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from termcolor import colored\n",
    "\n",
    "print(colored('红色', 'red'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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